Academia.eduAcademia.edu

Outline

Title
Abstract
Key Takeaways
Figures
Discussion
Creation of Speech Database
Speech Data Collection
Discussion and Future Work
Some Features of the Manually Tagged Data
Design Principles
Nyāya Terms Used in the Head-Modifier Analysis
Word Level Prakāratā-Viśes . Yatā Analysis Rules
Complete Prakāratā-Viśes . Yatā Analysis
Conclusions
Previous Work
Distinction of Present System Over Previous Ones
Methodology for the MBH Indexer
Database Connectivity
Evaluation Discussion of Tagsets
Conclusion and Further Research
Overview and Previous Research
The Test Design
Results and Evaluation
Conclusions and Future Developments
Quantitative Analysis
Conclusion and Future Work
Introduction
Ellipsis in Subject Noun Phrase
Conclusion
References
All Topics
Languages and Linguistics
Phonology

Sanskrit Compound Processor

Profile image of Amba KulkarniAmba KulkarniProfile image of Vipul MittalVipul MittalProfile image of Preeti ShuklaPreeti Shukla

2010, Sanskrit Computational Linguistics

https://doi.org/10.1007/978-3-642-17528-2_5
visibility

…

description

275 pages

link

1 file

Abstract
sparkles

AI

The document presents selected papers from the 4th International Sanskrit Computational Linguistics Symposium held at Jawaharlal Nehru University. It highlights various areas of research, including phonology, morphology, syntax, and machine translation. Key selected works include the application of Pān . ini's pratyāhāra technique to German and the development of a prototype text-to-speech system for Sanskrit.

Key takeaways
sparkles

AI

  1. Pānini's As.t.ādhyāyī outlines a complex system for Sanskrit grammar, emphasizing brevity and clarity in rule formulation.
  2. The proposed computational models utilize filters and dynamic attributes to effectively manage rule applications in Sanskrit grammar.
  3. Sanskrit's rich morphology and syntax present unique challenges for NLP tasks like parsing and anaphora resolution.
  4. Ellipsis in Sanskrit occurs frequently, with specific rules governing its application in verb and noun phrases.
  5. The development of a Sanskrit WordNet is underway, focusing on gloss creation and disambiguation for improved semantic understanding.
Figures (105)
Table 2. The sUP affixes
Table 2. The sUP affixes
‘igure [4] (right) shows a Hasse diagram of the concept lattice, i.e., a Hasse liagram of the set of the eight intersection sets ordered by the inverted subset elation. In the diagram the nodes (circles) correspond to the intersection sets und an edge between two nodes indicates that the set associated with the upper 1ode is a subset of the one associated with the lower node and that no other ntersection set is a superset of the set associated with the upper and a subset of she set associated with the lower node. The diagram is labeled as follows: each egment is written above the node corresponding to the smallest set to which it »elongs, e.g., c labels the node for the set {c}, b the node for the set {b}, and 1 the one for {a,b}. The labeled Hasse diagram can be read as an inheritance uierarchy: each node corresponds to the set of segments by which the node or me of its supernodes is labeled. E.g., the node for ‘class 4’ corresponds to the et {a, b,c}, the bottom node corresponds to {a, b,c, d}, and the top node to the mpty set.
‘igure [4] (right) shows a Hasse diagram of the concept lattice, i.e., a Hasse liagram of the set of the eight intersection sets ordered by the inverted subset elation. In the diagram the nodes (circles) correspond to the intersection sets und an edge between two nodes indicates that the set associated with the upper 1ode is a subset of the one associated with the lower node and that no other ntersection set is a superset of the set associated with the upper and a subset of she set associated with the lower node. The diagram is labeled as follows: each egment is written above the node corresponding to the smallest set to which it »elongs, e.g., c labels the node for the set {c}, b the node for the set {b}, and 1 the one for {a,b}. The labeled Hasse diagram can be read as an inheritance uierarchy: each node corresponds to the set of segments by which the node or me of its supernodes is labeled. E.g., the node for ‘class 4’ corresponds to the et {a, b,c}, the bottom node corresponds to {a, b,c, d}, and the top node to the mpty set.
Figure [shows a Hasse diagram with intersecting edges of the concept lattice of the formal context constituted by the four classes ‘devoicing output’, ‘devoic- ing input’, ‘nasal assimilation labial’, and ‘nasal assimilation velar’. According to [II] and [16], a lattice has no plane diagram if it is possible to gain the graph Y (i.e. the complete graph K°) from the graph of the diagram enlarged by an additional edge connecting the lowest and the top most node by removing some of the edges and contracting others. It is obvious that the graph in Figure (top) can be constructed from the one in Figure [6] by leaving out some of the edges. The sequence of drawings at the bottom of Figure[f|starts with the graph from the top of the figure enlarged by an additional edge connecting the top and the bottom node. Each remaining graph is gained from its left neighbor by con- tracting the emphasized edge (thick grey edge). As the final graph is isomorphic to the graph ®, the sequence proves that it is impossible to draw a diagram of the concept lattice in Figure [6] without intersecting edges. Hence, the concept lattice of the formal context of our collection of phonological classes is not pla- nar. It follows that it is impossible to construct pratyahara sutras for the formal context in which each sound segment occurs only once. Hence, analogously to the Sivasiitras for Sanskrit we are forced to repeat at least one sound segment for our pratyahara sutras for German. This. the next step towards nratriiaghara sitras for German is the identification
Figure [shows a Hasse diagram with intersecting edges of the concept lattice of the formal context constituted by the four classes ‘devoicing output’, ‘devoic- ing input’, ‘nasal assimilation labial’, and ‘nasal assimilation velar’. According to [II] and [16], a lattice has no plane diagram if it is possible to gain the graph Y (i.e. the complete graph K°) from the graph of the diagram enlarged by an additional edge connecting the lowest and the top most node by removing some of the edges and contracting others. It is obvious that the graph in Figure (top) can be constructed from the one in Figure [6] by leaving out some of the edges. The sequence of drawings at the bottom of Figure[f|starts with the graph from the top of the figure enlarged by an additional edge connecting the top and the bottom node. Each remaining graph is gained from its left neighbor by con- tracting the emphasized edge (thick grey edge). As the final graph is isomorphic to the graph ®, the sequence proves that it is impossible to draw a diagram of the concept lattice in Figure [6] without intersecting edges. Hence, the concept lattice of the formal context of our collection of phonological classes is not pla- nar. It follows that it is impossible to construct pratyahara sutras for the formal context in which each sound segment occurs only once. Hence, analogously to the Sivasiitras for Sanskrit we are forced to repeat at least one sound segment for our pratyahara sutras for German. This. the next step towards nratriiaghara sitras for German is the identification
features yield a total of 2'© = 65536 classes which can be described by feature sets, thus far more than by pratyaharas. Even if one drops the requirement that features should have acoustic, articulatory or auditory definitions and allows for unnatural features, a featural description of the 10 phonological classes cannot perform better than our pratyahara description. As none of our 10 classes can be described in terms of an intersection of some of the other classes, every featural description which is able to distinguish those 10 classes has to make use of at least 10 different features. A minimal featural description of the 10 classes would be to use the class identifiers (e.g., ‘devoicing input’, ‘devoicing output’) as fea- tures. Such a description would be minimal since none of the features would be reducible to other features. 10 features still yield a total of 21° = 1024 classes which can be described by feature sets; this is nearly four times more than by the pratyaharas we employed. One objection against our approach could be that we are onlv considering
features yield a total of 2'© = 65536 classes which can be described by feature sets, thus far more than by pratyaharas. Even if one drops the requirement that features should have acoustic, articulatory or auditory definitions and allows for unnatural features, a featural description of the 10 phonological classes cannot perform better than our pratyahara description. As none of our 10 classes can be described in terms of an intersection of some of the other classes, every featural description which is able to distinguish those 10 classes has to make use of at least 10 different features. A minimal featural description of the 10 classes would be to use the class identifiers (e.g., ‘devoicing input’, ‘devoicing output’) as fea- tures. Such a description would be minimal since none of the features would be reducible to other features. 10 features still yield a total of 21° = 1024 classes which can be described by feature sets; this is nearly four times more than by the pratyaharas we employed. One objection against our approach could be that we are onlv considering
Fig. 2. Sample speech used for training with its labels, spectrogram and wave form
Fig. 2. Sample speech used for training with its labels, spectrogram and wave form
Table 1. MOS Test Results
Table 1. MOS Test Results
Fig. 1. Constituency representation of qaderecdcaay
Fig. 1. Constituency representation of qaderecdcaay
Fig. 3. Compound Splitter: System Data Flow
Fig. 3. Compound Splitter: System Data Flow
Table 1. Rank-wise Distribution The complete rank wise distribution is given in Table[I]
Table 1. Rank-wise Distribution The complete rank wise distribution is given in Table[I]
Appendix-A : Sanskrit Compound Type and Sub-type Classification
Appendix-A : Sanskrit Compound Type and Sub-type Classification
Indian languages is described in Bharati, et. al. (1995; 85-100). This parser is modeled as a bipartite graph matching problem. A statistical parser for analysing Sanskrit is described in Hellwig (2008). The shallow parser of Huet (2006, 2009) uses bare minimum information of transitivity of a verb as a sub-categorisation frame and models it as a graph-matching algorithm. The main purpose of this shallow parser is to filter out non-sensical interpretations. It is therefore natural for Huet to develop small tools such as ‘ca’ handler with more priority to rule out non-grammatical solutions (rather than to develop a full-fledged parser). While designing a grammar based parser, two major design issued one has to address are: a) what should be the level of semantic analysis, and b) which relations to represent in the parsed output. In order to decide on these issues, in what follows, we first look at the Sanskrit grammar to see what kind of semantic relations can be extracted from a language string, precisely where is the information about these relations coded, and whether the extracted relations are from primary sources or secondary. Later we discuss the issues the mechanical processing throws up, and the possible ways to handle them. Based on these observations, we decide various design parameters. The next section discusses mathematical formulation of the problem, its implementation and finally its performance analysis.
Indian languages is described in Bharati, et. al. (1995; 85-100). This parser is modeled as a bipartite graph matching problem. A statistical parser for analysing Sanskrit is described in Hellwig (2008). The shallow parser of Huet (2006, 2009) uses bare minimum information of transitivity of a verb as a sub-categorisation frame and models it as a graph-matching algorithm. The main purpose of this shallow parser is to filter out non-sensical interpretations. It is therefore natural for Huet to develop small tools such as ‘ca’ handler with more priority to rule out non-grammatical solutions (rather than to develop a full-fledged parser). While designing a grammar based parser, two major design issued one has to address are: a) what should be the level of semantic analysis, and b) which relations to represent in the parsed output. In order to decide on these issues, in what follows, we first look at the Sanskrit grammar to see what kind of semantic relations can be extracted from a language string, precisely where is the information about these relations coded, and whether the extracted relations are from primary sources or secondary. Later we discuss the issues the mechanical processing throws up, and the possible ways to handle them. Based on these observations, we decide various design parameters. The next section discusses mathematical formulation of the problem, its implementation and finally its performance analysis.
Table 1. Relations The relations used, along with their ranks are given in Table 1
Table 1. Relations The relations used, along with their ranks are given in Table 1
Stapll WiC Is tlle WiCallils OL seiucice, In fact, our goal is much bigger than just solving the problem of sabdabodha where one only tries to analyze the meaning of a sentence. Our goal is to con- struct a computational ontology which is foundational as well as generative in nature. We are aiming at generative graph grammar on which comprehensive ontology interpreter can be constructed. Once that task is accomplished, one could apply it to solve domain-specific problems like sabdabodha . Hence the main focus of the paper is on the building of graph grammar suitable for on- tology applications. Idea is to construct computational environment such that applications like sabdabodha can be built. Most of the existing ontologies follow top-down approach they start with the names of the entities (real as well as imaginary) existing in the universe, they try to classify these names and in turn try to classify the actual objects in the universe. But this approach has a problem since in the process of classifying names, one cannot help placing a name under two classes. In other words one name/class might have to have two parent classes. This is called crossing. Fot instance if one has the classes ‘flying objects’ and ‘horses’ in one’s classificatior and now if one encounters a new object called ‘flying horse’, in which of the above classes should he place this object? Obviously, both classes should be its parents since it has the features of both the classes, and hence there is crossing. A good ontological classification is one in which there are no crossings. Having saic this, we now elucidate an alternative bottom-up approach which we take to build not just any ontology but a formal, foundational and generative ontology. Fo1 this we utilized formalization of Vaisestka ontology which is a comprehensive foundational ontology. While formalizing it, there was some difference noted witl classical Vaisesika traditions, hence our approach has been called Neo-Vaisesike Formal Ontology (NVFO).
Stapll WiC Is tlle WiCallils OL seiucice, In fact, our goal is much bigger than just solving the problem of sabdabodha where one only tries to analyze the meaning of a sentence. Our goal is to con- struct a computational ontology which is foundational as well as generative in nature. We are aiming at generative graph grammar on which comprehensive ontology interpreter can be constructed. Once that task is accomplished, one could apply it to solve domain-specific problems like sabdabodha . Hence the main focus of the paper is on the building of graph grammar suitable for on- tology applications. Idea is to construct computational environment such that applications like sabdabodha can be built. Most of the existing ontologies follow top-down approach they start with the names of the entities (real as well as imaginary) existing in the universe, they try to classify these names and in turn try to classify the actual objects in the universe. But this approach has a problem since in the process of classifying names, one cannot help placing a name under two classes. In other words one name/class might have to have two parent classes. This is called crossing. Fot instance if one has the classes ‘flying objects’ and ‘horses’ in one’s classificatior and now if one encounters a new object called ‘flying horse’, in which of the above classes should he place this object? Obviously, both classes should be its parents since it has the features of both the classes, and hence there is crossing. A good ontological classification is one in which there are no crossings. Having saic this, we now elucidate an alternative bottom-up approach which we take to build not just any ontology but a formal, foundational and generative ontology. Fo1 this we utilized formalization of Vaisestka ontology which is a comprehensive foundational ontology. While formalizing it, there was some difference noted witl classical Vaisesika traditions, hence our approach has been called Neo-Vaisesike Formal Ontology (NVFO).
The above details can be concisely captured in the following diagram. If the match m fixes an occurrence of L in a given graph G, then G 24H denotes the direct derivation where p is applied to G leading to a derived graph HZ. Intuitively, H is obtained by replacing the occurrence of L in G by R. Negative Application Conditions (NACs). Rules can have exceptions - it may not be likely to apply a rule in some particular cases. Those cases/conditions can also be depicted in the form of graphs. Hence each rule may (or may not) have one or more NACs. So a rule will be applied only when its LHS matches with some subgraph of the host graph, and none of its NACs matches with any subgraph of the host graph.
The above details can be concisely captured in the following diagram. If the match m fixes an occurrence of L in a given graph G, then G 24H denotes the direct derivation where p is applied to G leading to a derived graph HZ. Intuitively, H is obtained by replacing the occurrence of L in G by R. Negative Application Conditions (NACs). Rules can have exceptions - it may not be likely to apply a rule in some particular cases. Those cases/conditions can also be depicted in the form of graphs. Hence each rule may (or may not) have one or more NACs. So a rule will be applied only when its LHS matches with some subgraph of the host graph, and none of its NACs matches with any subgraph of the host graph.
Rule 1 (Start). We start by replacing the start node, ‘G’, with anonymous entity. ‘BE’, and variable node, ‘X’, which are self-linked.
Rule 1 (Start). We start by replacing the start node, ‘G’, with anonymous entity. ‘BE’, and variable node, ‘X’, which are self-linked.
Rule 7 (MereologyConjunct). Here the middle graph represents LHS of this rule and the rightmost one, its RHS. It says that when some entity inheres in two othe entities, a conjunct can be created in between them on the condition (NAC) that there is already no conjunct between them, which is represented by the leftmost graph in the above image. This rule is to model the conjunct between parts of a whole. Rule 6 (Disjunct). LHS is replaced by separable punctuator where the new entity (£) and the old entity (1:£) are in disjunct with each other. In the process ‘D’ is generated which inheres in both the entities.
Rule 7 (MereologyConjunct). Here the middle graph represents LHS of this rule and the rightmost one, its RHS. It says that when some entity inheres in two othe entities, a conjunct can be created in between them on the condition (NAC) that there is already no conjunct between them, which is represented by the leftmost graph in the above image. This rule is to model the conjunct between parts of a whole. Rule 6 (Disjunct). LHS is replaced by separable punctuator where the new entity (£) and the old entity (1:£) are in disjunct with each other. In the process ‘D’ is generated which inheres in both the entities.
Rule 8 (MereologyDisjunct). It says that when some entity inheres in two other entities, a disjunct can be created in between them on the condition (NAC) that there is already no disjunct between them.
Rule 8 (MereologyDisjunct). It says that when some entity inheres in two other entities, a disjunct can be created in between them on the condition (NAC) that there is already no disjunct between them.
Rule 1 (DefineSubstantialWhole). A substantial whole is one in which something inheres, and also it itself inheres in some other entities. Hence ‘E” is replaced by ‘S,,’ if at least one entity inheres in it, and it itself inheres in minimum two other entities (It has to inhere in minimum two entities for it to be differentiated from Qualities and Actions which can inhere only in one entity).
Rule 1 (DefineSubstantialWhole). A substantial whole is one in which something inheres, and also it itself inheres in some other entities. Hence ‘E” is replaced by ‘S,,’ if at least one entity inheres in it, and it itself inheres in minimum two other entities (It has to inhere in minimum two entities for it to be differentiated from Qualities and Actions which can inhere only in one entity).
Rule 9 (DefineQuality(a)). In a Quality at least one Universal inheres, and it itself inheres in one and only one whole (its inherence in two or more wholes is handled in NACs).
Rule 9 (DefineQuality(a)). In a Quality at least one Universal inheres, and it itself inheres in one and only one whole (its inherence in two or more wholes is handled in NACs).
inheres in one and only one whole.
inheres in one and only one whole.
Let’s consider the sentence, ‘Jug is on the table’. Using standard NLP tech- niques, one can identify jug and table to be some objects and that table is used in the locative sense. This is all the meaning that could be drawn when this sentence is considered as such. But when we replace ‘S,,,’ in the above structure with ‘jug’, and another ‘S',,’ (which is in contact with the earlier S,,) with ‘table’, we can draw the following inferences:
Let’s consider the sentence, ‘Jug is on the table’. Using standard NLP tech- niques, one can identify jug and table to be some objects and that table is used in the locative sense. This is all the meaning that could be drawn when this sentence is considered as such. But when we replace ‘S,,,’ in the above structure with ‘jug’, and another ‘S',,’ (which is in contact with the earlier S,,) with ‘table’, we can draw the following inferences:
Prakarata-vigesyata analysis within tinanta word: In the case of verbs, root meaning is “head” and suffix meanings are modifiers. The tinanta suffix has four meanings (1) ka@raka, (2) purusa, (3) vacana and (4) kala. Tense or mood is related with the action and person and number are related with karakas expressed by a tin suffix. Overall two karakas can be expressed by a tin suffix and in the
Prakarata-vigesyata analysis within tinanta word: In the case of verbs, root meaning is “head” and suffix meanings are modifiers. The tinanta suffix has four meanings (1) ka@raka, (2) purusa, (3) vacana and (4) kala. Tense or mood is related with the action and person and number are related with karakas expressed by a tin suffix. Overall two karakas can be expressed by a tin suffix and in the
The tinianta suffix has four meanings (1) kdraka, (2) purusa, (3) vacana and (4) kala. The subanta ending in the first case-ending is identified with the meaning karaka of the tinanta suffix. Therefore, it is placed or moved in the karaka meaning. Other two meanings of the tinianta suffix namely, purusa, and vacana become the prakara of the subanta in the nominative case, linked with the karaka meaning of the tinanta suffix. The complete sentence meaning after prakarata and vigesyatad analysis will be as follows.(Table.4) If the sentence is transformed to or is in 1 the passive or impersonal | form then ee ee | lem ry rH ¥ 7— _ “- q
The tinianta suffix has four meanings (1) kdraka, (2) purusa, (3) vacana and (4) kala. The subanta ending in the first case-ending is identified with the meaning karaka of the tinanta suffix. Therefore, it is placed or moved in the karaka meaning. Other two meanings of the tinianta suffix namely, purusa, and vacana become the prakara of the subanta in the nominative case, linked with the karaka meaning of the tinanta suffix. The complete sentence meaning after prakarata and vigesyatad analysis will be as follows.(Table.4) If the sentence is transformed to or is in 1 the passive or impersonal | form then ee ee | lem ry rH ¥ 7— _ “- q
prakrtyartha-ramanistha-prakarata-nirt pita-pratvayartha-kartr-nistha-visesvata- nistha-prakarata-nirt pita-prathamapurusa-ekavacana-nistha-prakarata-niru pita- prakrtvartha-gramanistha-prakarata-nirt pita-pratvavartha-gramanistha-visesvata- abhinna-karmanistha-visesvata-nist ha-prakarata-niru pita-vartamana-nistha-prakarata- OR yD Se) Ne ey Eee Te eee Seen. ee ee, ee ew |
prakrtyartha-ramanistha-prakarata-nirt pita-pratvayartha-kartr-nistha-visesvata- nistha-prakarata-nirt pita-prathamapurusa-ekavacana-nistha-prakarata-niru pita- prakrtvartha-gramanistha-prakarata-nirt pita-pratvavartha-gramanistha-visesvata- abhinna-karmanistha-visesvata-nist ha-prakarata-niru pita-vartamana-nistha-prakarata- OR yD Se) Ne ey Eee Te eee Seen. ee ee, ee ew |
Even though the suffix in the impersonal verb conveys two meanings (1) kala, tense and (2) bhava, the later meaning does not figure in the head-modifier analysis because the meaning bhava actually means the action in general and therefore is connected to the meaning of the verbal root with non-difference (abheda). The head-modifier analysis of the sentence of the impersonal voice will be as shown below. (Table.6)
Even though the suffix in the impersonal verb conveys two meanings (1) kala, tense and (2) bhava, the later meaning does not figure in the head-modifier analysis because the meaning bhava actually means the action in general and therefore is connected to the meaning of the verbal root with non-difference (abheda). The head-modifier analysis of the sentence of the impersonal voice will be as shown below. (Table.6)
Table 8. Hierarchical bracket parsing of the active voice sentence ramah gramam gacchati Hierarchical Bracket Parsing
Table 8. Hierarchical bracket parsing of the active voice sentence ramah gramam gacchati Hierarchical Bracket Parsing
Table 6. Groups and Characters Present in the Groups
Table 6. Groups and Characters Present in the Groups
Table 7. Partial Result Set for Approximate Matching
Table 7. Partial Result Set for Approximate Matching
Table 1. Structure of Mahabharata (According to the BORI’s critical edition) this point. MBh is neither a history in the modern sense of the term, nor chronicle. But it stands in incomparable isolation, defying all definitions. It is’ veritable encyclopaedia comprising heterogeneous material from all branches c knowledge. Taking the core-story of the feud between two branches of a roye family and the circumstances leading to a catastrophic war, several branche of knowledge including philosophy, law, ethics, statecraft, warfare, history an ethnology are embodied in its structure. The MBh is a comment on the huma condition with all its richness, complexity and subtlety. The MBh is the source c many compositions like- abhijndanasakuntalam of Kalidasa, naisadhiya-caritar of sriharsa etc. It is the text that is most sought for in order to enrich cultura social and any other type of knowledge about Indian civilization. While on the one hand, the text is very important, on the other, it is so hug
Table 1. Structure of Mahabharata (According to the BORI’s critical edition) this point. MBh is neither a history in the modern sense of the term, nor chronicle. But it stands in incomparable isolation, defying all definitions. It is’ veritable encyclopaedia comprising heterogeneous material from all branches c knowledge. Taking the core-story of the feud between two branches of a roye family and the circumstances leading to a catastrophic war, several branche of knowledge including philosophy, law, ethics, statecraft, warfare, history an ethnology are embodied in its structure. The MBh is a comment on the huma condition with all its richness, complexity and subtlety. The MBh is the source c many compositions like- abhijndanasakuntalam of Kalidasa, naisadhiya-caritar of sriharsa etc. It is the text that is most sought for in order to enrich cultura social and any other type of knowledge about Indian civilization. While on the one hand, the text is very important, on the other, it is so hug
Table 2. The ‘shloka’ table
Table 2. The ‘shloka’ table
Table 5. The ‘upaparva’ table Table 4. The ‘akhyana’ table
Table 5. The ‘upaparva’ table Table 4. The ‘akhyana’ table
Development of the MBh Indexer
Development of the MBh Indexer
Step II: MBh Indexer and Database. At this step, the indexer makes an indexed list of exact and partially matching words. Getting the query as an input, the indexer, after a light preprocessing, sends it to the database. If the word has its occurrence in the database, the system gives the output.
Step II: MBh Indexer and Database. At this step, the indexer makes an indexed list of exact and partially matching words. Getting the query as an input, the indexer, after a light preprocessing, sends it to the database. If the word has its occurrence in the database, the system gives the output.
Fig.1. Multi-tiered architecture of the MBh indexing system
Fig.1. Multi-tiered architecture of the MBh indexing system
Fig. 4. Search page of the MBh indexing system Fig. 3. Homepage of the MBh Indexing System
Fig. 4. Search page of the MBh indexing system Fig. 3. Homepage of the MBh Indexing System
The front-end of the system is developed in utf-8 enabled Java Server Pages (JSP) and HTML. The front-end of the software enables the user to interact with the indexing system with the help of Apache Tomcat web-server. The JSP technology helps to create web based applications by combining Java code with HTML. The web server runs the Java code and displays the results as HTML. For this system, there are two JSP pages, one is the main search page and the other is the cross-referential search page which searches the searched query in different online lexical and linguistic resources. The snapshots of the indexing system are as follow:
The front-end of the system is developed in utf-8 enabled Java Server Pages (JSP) and HTML. The front-end of the software enables the user to interact with the indexing system with the help of Apache Tomcat web-server. The JSP technology helps to create web based applications by combining Java code with HTML. The web server runs the Java code and displays the results as HTML. For this system, there are two JSP pages, one is the main search page and the other is the cross-referential search page which searches the searched query in different online lexical and linguistic resources. The snapshots of the indexing system are as follow:
Fig. 8. Diagram of the interconnection between the database tables A design of the indexing system of MBh database is given below: 5.4 Database Connectivity
Fig. 8. Diagram of the interconnection between the database tables A design of the indexing system of MBh database is given below: 5.4 Database Connectivity
to lose important information. This tagset does not include attributes of lexical categories such as gender, number, person etc. Apart from this the tags for incorporated adjective and adverb are not applicable in Sanskrit. In Sanskrit, like noun and verb, Particles also require more tags as they have different and valuable function in the language, so in order to suit Sanskrit this tagset will have to be modified significantly. Keeping in mind the complex morphology of Sanskrit this flat tagset will hardly be able to do WSD.
to lose important information. This tagset does not include attributes of lexical categories such as gender, number, person etc. Apart from this the tags for incorporated adjective and adverb are not applicable in Sanskrit. In Sanskrit, like noun and verb, Particles also require more tags as they have different and valuable function in the language, so in order to suit Sanskrit this tagset will have to be modified significantly. Keeping in mind the complex morphology of Sanskrit this flat tagset will hardly be able to do WSD.
Table 2. JNU-Sanskrit Tagset This is a flat and fine-grained tagset capturing linguistic features accordi (o the traditional Sanskrit grammar. Its coverage is very high. Almost eve <ind of linguistic item was treated discretely (especially taddhita and kridan constructions). This tagset is very informative but we assume that its specifici will suffer while tagging the data. For instance, among noun tags there are 8 ta and the basis of counting them separately seems to be the special morpholo; ‘or these items like Patronymic Noun (e.g. raghavah, janaki), Metronymic Nov e.g. parthah, saumitrih), (both tags could be treated as Proper Noun), Age Noun (e.g. kartuh, adhyapakah) (could be treated as Common Noun). Likewi ander Compound category there are 14 types. Many of which could be clubbe
Table 2. JNU-Sanskrit Tagset This is a flat and fine-grained tagset capturing linguistic features accordi (o the traditional Sanskrit grammar. Its coverage is very high. Almost eve <ind of linguistic item was treated discretely (especially taddhita and kridan constructions). This tagset is very informative but we assume that its specifici will suffer while tagging the data. For instance, among noun tags there are 8 ta and the basis of counting them separately seems to be the special morpholo; ‘or these items like Patronymic Noun (e.g. raghavah, janaki), Metronymic Nov e.g. parthah, saumitrih), (both tags could be treated as Proper Noun), Age Noun (e.g. kartuh, adhyapakah) (could be treated as Common Noun). Likewi ander Compound category there are 14 types. Many of which could be clubbe
Table 3. LDCIL Tagset under other categories as well. For example, the words like rajapurusah and devendrah have been assigned Determinative Compound (genitive) which could be very well given Common noun and Proper noun respectively. The division of participle tags is also very lengthy; kridanta vartamana has two separate tags which does not seem serving any purpose and the same is with kridanta vidhyarthaka which has 3 tags.
Table 3. LDCIL Tagset under other categories as well. For example, the words like rajapurusah and devendrah have been assigned Determinative Compound (genitive) which could be very well given Common noun and Proper noun respectively. The division of participle tags is also very lengthy; kridanta vartamana has two separate tags which does not seem serving any purpose and the same is with kridanta vidhyarthaka which has 3 tags.
Table 4. IL-POSTS Sanskrit tagset categories and their Types Table 5. IL-POSTS Sanskrit tagset attributes and their values
Table 4. IL-POSTS Sanskrit tagset categories and their Types Table 5. IL-POSTS Sanskrit tagset attributes and their values
In the introduction, we mentioned the theory that the amount of training data influences the analysis accuracy. To assess this theory, the analysis of the RASAMANJARI was repeated using statistical key values that were calculated from randomly selected subsets of the text corpus. Figure[3] displays graphically the influence between the amount of training data and the analysis quality. Although the analysis accuracy obviously increases with the amount of training data, we achieve comparatively high rates of accuracy even when only 20% of the corpus are used for training. Therefore, it seems that substantial increases in the accuracy of the tagger cannot be achieved by increasing the size of the corpus, but only by improving the analysis procedures. Figure [J] displays the boxplots of H, Hpzx and Epog. Since the analysis of Sanskrit differs strongly from that of other languages, H and Hrpx are hardly comparable with rates recorded in previous research. Obviously, however, both A and Hyex need significant improvements in future versions of the program. If we take into account the complex considerations that the linguistic phenomena of composite creation and bahuvrihi make necessary for POS tagging of Sanskrit, the POS accuracy of SanskritTagger compares acceptably with POS error rates reported for less complex languages (J4e Pos = 0.090, o¢ pos = 0.035). To go deeper into the details of table [] we should try to determine the influence of literary features on the analysis quality. If lexical richness and the average nhrace lencth are used to vet an estimation of the complexity of a literarv
In the introduction, we mentioned the theory that the amount of training data influences the analysis accuracy. To assess this theory, the analysis of the RASAMANJARI was repeated using statistical key values that were calculated from randomly selected subsets of the text corpus. Figure[3] displays graphically the influence between the amount of training data and the analysis quality. Although the analysis accuracy obviously increases with the amount of training data, we achieve comparatively high rates of accuracy even when only 20% of the corpus are used for training. Therefore, it seems that substantial increases in the accuracy of the tagger cannot be achieved by increasing the size of the corpus, but only by improving the analysis procedures. Figure [J] displays the boxplots of H, Hpzx and Epog. Since the analysis of Sanskrit differs strongly from that of other languages, H and Hrpx are hardly comparable with rates recorded in previous research. Obviously, however, both A and Hyex need significant improvements in future versions of the program. If we take into account the complex considerations that the linguistic phenomena of composite creation and bahuvrihi make necessary for POS tagging of Sanskrit, the POS accuracy of SanskritTagger compares acceptably with POS error rates reported for less complex languages (J4e Pos = 0.090, o¢ pos = 0.035). To go deeper into the details of table [] we should try to determine the influence of literary features on the analysis quality. If lexical richness and the average nhrace lencth are used to vet an estimation of the complexity of a literarv
Table 1. Tokens and types in each kandas Polysemy. Amarakosga has 4,017 synsets. Some of the words fall under mor than one synset, and thus are ambiguous. Most of these polysemous words belon; to the nanarthavarga of the third kanda which lists the polysemous words alpha betically according to their endings. The polysemy distribution in the Amarakos. is summarised in Table 2. There is only one word hari in Amarakosga which ha as many as 14 senses, the word antara belongs to 13 synsets, and the word g has 12 synsets. We note that almost 65% words (7459 words) belong to a singl synset and thus are not ambiguous.
Table 1. Tokens and types in each kandas Polysemy. Amarakosga has 4,017 synsets. Some of the words fall under mor than one synset, and thus are ambiguous. Most of these polysemous words belon; to the nanarthavarga of the third kanda which lists the polysemous words alpha betically according to their endings. The polysemy distribution in the Amarakos. is summarised in Table 2. There is only one word hari in Amarakosga which ha as many as 14 senses, the word antara belongs to 13 synsets, and the word g has 12 synsets. We note that almost 65% words (7459 words) belong to a singl synset and thus are not ambiguous.
Table 2. Polysemy distribution from the meta-language indicating the gender of the following word siva to be feminine. In addition to these general guidelines, in the lingadisangrahavarga Amarasimha gives certain grammatical and phonological clues for deciding the gender of a word. In the event of absence of any rule, the gender of the remaining words in 2.5.5, constituting two compound words” bhurimayagomayumrgadhurta kah” and ” srgalavancakakrostupherupheravajambukah” is inferred to be mascu- line from their compounding-forms. The avvavavarea, lists svnsets consistine of indeclinables.
Table 2. Polysemy distribution from the meta-language indicating the gender of the following word siva to be feminine. In addition to these general guidelines, in the lingadisangrahavarga Amarasimha gives certain grammatical and phonological clues for deciding the gender of a word. In the event of absence of any rule, the gender of the remaining words in 2.5.5, constituting two compound words” bhurimayagomayumrgadhurta kah” and ” srgalavancakakrostupherupheravajambukah” is inferred to be mascu- line from their compounding-forms. The avvavavarea, lists svnsets consistine of indeclinables.
Table 3. Words and references of the svarga-synset
Table 3. Words and references of the svarga-synset
Table 4. Example of Head-Word The relations are among various Head Words and are marked as records. Each record corresponds to one synset ID. The first field of each record consists of the synset ID, and remaining six fields correspond to the Head Words that bear a relation of is_a_part_of (avayavavayavi), is.a_kind_of(pardparajati), janya- janaka-bhava, pati-patni-bhava, sva-svami-bhava, ajivika with the synset ID in the first field.
Table 4. Example of Head-Word The relations are among various Head Words and are marked as records. Each record corresponds to one synset ID. The first field of each record consists of the synset ID, and remaining six fields correspond to the Head Words that bear a relation of is_a_part_of (avayavavayavi), is.a_kind_of(pardparajati), janya- janaka-bhava, pati-patni-bhava, sva-svami-bhava, ajivika with the synset ID in the first field.
Table 6. Example of is_a_kind_of relations
Table 6. Example of is_a_kind_of relations
Table 8. Example of Pati-patni relation
Table 8. Example of Pati-patni relation
Table 10. Example of Ajzvika relation
Table 10. Example of Ajzvika relation
Table 11. Relational statistics
Table 11. Relational statistics
Fig. 2. Histogram showing the WSD accuracy across domains for Hindi Words Table 2. WSD accuracy across domains for Hindi Words
Fig. 2. Histogram showing the WSD accuracy across domains for Hindi Words Table 2. WSD accuracy across domains for Hindi Words
Table 5. Possible synsets for the word hari in the present Sanskrit Wordnet
Table 5. Possible synsets for the word hari in the present Sanskrit Wordnet
Table 3. Differential Divergence
Table 3. Differential Divergence
Table 4. Alternative Divergence 2.5 Non-karaka Divergence
Table 4. Alternative Divergence 2.5 Non-karaka Divergence
Table 5. Non-karaka Divergence 2.6 Hindi Specific Divergences
Table 5. Non-karaka Divergence 2.6 Hindi Specific Divergences
Table 1. Sanskrit anaphora Pronominal Anaphors. The major classification in pronominal is the first, second, and third person pronoun. First and second person are commonly used as deictic, though they are used in discourse. The third person pronoun is the most commonly used anaphora in a language. In Sanskrit tat is the third person pronoun which has the following separate forms in different cases for masculine and feminine gender|] rT 4 row . r << whee wih 7 a | as 4 =a os AMsyye 4Y
Table 1. Sanskrit anaphora Pronominal Anaphors. The major classification in pronominal is the first, second, and third person pronoun. First and second person are commonly used as deictic, though they are used in discourse. The third person pronoun is the most commonly used anaphora in a language. In Sanskrit tat is the third person pronoun which has the following separate forms in different cases for masculine and feminine gender|] rT 4 row . r << whee wih 7 a | as 4 =a os AMsyye 4Y
Table 2. Forms of pronominal tat in different cases
Table 2. Forms of pronominal tat in different cases
Fig. 1. Computational Structure of Astadhyayt with Asiddhatva Principle
Fig. 1. Computational Structure of Astadhyayt with Asiddhatva Principle
Fig. 6. Step 3: Selecting one rule from the set of applicable rules
Fig. 6. Step 3: Selecting one rule from the set of applicable rules

Related papers

A distributed platform for Sanskrit Processing
Amba Kulkarni

Sanskrit, the classical language of India, presents specific challenges for computational linguistics: exact phonetic transcription in writing that obscures word boundaries, rich morphology and an enormous corpus, among others. Recent international cooperation has developed innovative solutions to these problems and significant resources for linguistic research. Solutions include efficient segmenting and tagging algorithms and dependency parsers based on constraint programming. The integration of lexical resources, text archives and linguistic software is achieved by distributed interoperable Web services. Resources include a morphological tagger and tagged corpus.

View PDFchevron_right
A survey on the ongoing and future research in Sanskrit Computational Linguistics
Shreyas Gokhale

Sanskrit Computational Linguistics is an interdisciplinary area of study where the computational techniques and principles are applied for the linguistic analysis of Sanskrit texts and extracting information from the grammatical tradition of Sanskrit to enrich the linguistic and computational studies in general. This paper gives a survey of the ongoing work in this field by categorizing the research activities and time-line of the work done in the discipline. It further describes the future challenges in this subject to be met and its scope into other disciplines of study.

View PDFchevron_right
Proceedings of the 3rd International Symposium on Sanskrit Computational Linguistics
Amba Kulkarni

2008

cal tradition for Pān. ini's grammar, the As. t .ā dhyāyī. An insight due to Bhartr. hari shows how they can be satisfactorily analyzed. This insight was recast using the notion of an argument frame, a generalization of subcategorization frame. A bonus of this solution is that it provides insight into well-know properties of the exocentric compounds of Classical Sanskrit, properties which exocentric compounds in English do not have.

View PDFchevron_right
The syntax of Sanskrit compounds
John Lowe

Language, 2015

Classical Sanskrit is well known for making extensive use of compounding. I argue, within a lexicalist framework, that the major rules of compounding in Sanskrit can be most appropriately characterized in syntactic, not morphological, terms. That is, Classical Sanskrit 'compounds' are in fact very often syntactic phrases. The syntactic analysis proposed captures the fact that compound formation is closer to a morphological process than other aspects of syntax, and so permits some acknowledgment of the gradient nature of the word-phrase divide, even within a strictly lexicalist theory.*

View PDFchevron_right
The tripartite word-formation scheme applied to Sanskrit compounding
Ramón F Zacarías Ponce de León

International Journal of Sanskrit Research, 2018

In this paper we present an alternative model to the Morpheme and Lexeme based explanation of external predication of the Sanskrit bahuvrīhi compounds. An ‘exocentric head’ is problematic because it leads us to a rather strange geometric analysis in which ́heads ́ are outside the structure they supposed to head (Benveniste, 1977). Also because it is not an explicative concept in Morphology (Bauer 2008, 2016). Hence we depart from a formal criterion that classifies all compounds as endocentric and a semantic criterion according to which external predication can be viewed as metonymic instead of possessive, and can therefore be explained in cognitive terms by means of the notion of reference point. Then, we move to a definition of ‘focus’ as the whole word that percolates its grammatical functions to a tripartite morphological correspondence. The suggestion here in short is that it is better to term possessive compounds as metonymic integrated words and explain them as a kind of tripartite interrelationship according to the Word-based model that takes just the whole word as it unit of analysis.

View PDFchevron_right
Semantic Processing of Compounds in Indian Languages
Amba Kulkarni

Compounds occur very frequently in Indian Languages. There are no strict orthographic conventions for compounds in modern Indian Languages. In this paper, Sanskrit compounding system is examined thoroughly and the insight gained from the Sanskrit grammar is applied for the analysis of compounds in Hindi and Marathi. It is interesting to note that compounding in Hindi deviates from that in Sanskrit in two aspects. The data analysed for Hindi does not contain any instance of Bahuvrīhi (exo-centric) compound. Second, Hindi data presents many cases where quite a lot of compounds require a verb as well as vibhakti(a case marker) for its paraphrasing. Compounds requiring a verb for paraphrasing are termed as madhyama-pada-lopī in Sanskrit, and they are found to be rare in Sanskrit.

View PDFchevron_right
Sanskrit Compounds and the Architecture of the Grammar
Adriana Molina Munoz
View PDFchevron_right
A Corpus-based Decompounding in Sanskrit
Puneet Mamgain

2019

Unlike English, in highly inflected Indian languages like Bengali, Marathi, and Sanskrit, compound words are not multi-word expressions but created by combining two or more simple words without any orthographic separation. A compound word with unmarked word boundaries creates a problem for many computational tasks. Splitting compound words improves performances in Machine Translation, and Information Retrieval by reducing out-of-vocabulary words in the dictionary. So far, a number of decompounding techniques have been applied in European languages like German, Dutch, and Scandinavian. In this work, we apply a corpus-based decompounding technique in Sanskrit and improve splitting accuracy by applying various ranking methods. We evaluate the performance by different ranking methods against a gold standard in terms of Precision, Recall, and F-measure.

View PDFchevron_right
Semantic Processing of Compounds in Indian Languages Amba kulkarni
Amba Kulkarni, KULKARNI MALHAR, soma Paul

Compounds occur very frequently in Indian Languages. There are no strict orthographic conventions for compounds in modern Indian Languages. In this paper, Sanskrit compounding system is examined thoroughly and the insight gained from the Sanskrit grammar is applied for the analysis of compounds in Hindi and Marathi. It is interesting to note that compounding in Hindi deviates from that in Sanskrit in two aspects. The data analysed for Hindi does not contain any instance of Bahuvrīhi (exo-centric) compound. Second, Hindi data presents many cases where quite a lot of compounds require a verb as well as vibhakti(a case marker) for its paraphrasing. Compounds requiring a verb for paraphrasing are termed as madhyama-pada-lopī in Sanskrit, and they are found to be rare in Sanskrit.

View PDFchevron_right
NATURAL LANGUAGE PROCESSING AND SANSKRIT
IAEME Publication

IAEME PUBLICATION, 2014

Sanskrit considered being the mother of all languages possesses a rich grammar which was penned by Panini around 2500 years ago. The dual case is a unique and beautiful feature of Sanskrit which is not present in any other language other than Sanskrit. The importance of dual case is ignored in almost all the languages which lead to confusion while processing dual and plural but this ambiguity is also removed in the highly efficient and organized Sanskrit grammar. This research paper focuses on this Indian treasure and suggests its use for to-day's computer applications. It explores diverse key characteristics of NLP, various problems encountered in NLP are and how Sanskrit successfully overcomes these limitations and fulfills all the requirements of a Natural Language Processor.

View PDFchevron_right

References (323)

  1. Cardona, G.: Studies in Indian grammarians I: The method of description reflected in the Śiva-Sūtras. Transactions of the American Philosophical Society 59(1), 3-48 (1969)
  2. Chomsky, N., Halle, M.: The Sound Pattern of English. Harper and Row, New York (1968)
  3. Deshpande, M.M.: Ancient Indian phonetics. In: Koerner, E.F.K., Asher, R.E. (eds.) Concise History of the Language Sciences: From the Sumerians to the Cog- nitivists, pp. 72-77. Elsevier, Oxford (1995)
  4. Ganter, B., Wille, R.: Formal Concept Analysis. Mathematical Foundations, Berlin (1999)
  5. Hall, T.A.: Phonologie: Eine Einführung. Walter de Gruyter, Berlin (2000)
  6. Hamann, S.: The Phonetics and Phonology of Retroflexes. Ph.D. thesis, Utrecht Institute of Linguistics (2003)
  7. Jakobson, R., Fant, G., Halle, M.: Preliminaries to Speech Analysis: The Distinctive Features and their Correlates. MIT Press, Cambridge (1952)
  8. Kenstowicz, M.: Phonology in Generative Grammar. Blackwell, Cambridge (1994)
  9. Kiparsky, P.: Economy and the construction of the Śivasūtras. In: Deshpande, M.M., Bhate, S. (eds.) Pān . inian Studies, Ann Arbor, Michigan (1991)
  10. Kornai, A.: The generative power of feature geometry. Annals of Mathematics and Artificial Intelligence (8), 37-46 (1993)
  11. Kuratowski, K.: Sur le problème des courbes gauches en topologie. Fundamenta Mathematicae 15, 271-283 (1930)
  12. Misra, V.N.: The Descriptive Technique of Pān . ini. An Introduction. Mouton & Co., The Hague (1966)
  13. Petersen, W.: A mathematical analysis of Pān . ini's Śivasūtras. Journal of Logic, Language, and Information 13(4), 471-489 (2004)
  14. Petersen, W.: Zur Minimalität von Pān . inis Śivasūtras -Eine Untersuchung mit Methoden der Formalen Begriffsanalyse. Ph.D. thesis, University of Düsseldorf (2008)
  15. Petersen, W.: On the construction of Śivasūtras-alphabets. In: Kulkarni, A., Huet, G. (eds.) Sanskrit Computational Linguistics. LNCS (LNAI), vol. 5406, pp. 78-97.
  16. Springer, Heidelberg (2009)
  17. Platt, C.R.: Planar lattices and planar graphs. Journal of Combinatorial Theory (B) 21, 30-39 (1976)
  18. Staal, F.J.: A method of linguistic description. Language 38, 1-10 (1962)
  19. Staal, F.J.: The Sanskrit of science. Journal of Indian Philosophy 23(1), 73-127 (1995)
  20. Vennemann, T.: The German velar nasal: a case for abstract phonology. Phonetica 22(65-81) (1970)
  21. Whitney, W.D.: Sanskrit Grammar. Harvard University Press, Cambridge (1889)
  22. Wiese, R.: Phonology of German. Oxford University Press, Oxford (1996) References
  23. Formant Synthesis by Thomas Styger and Eric Keller, Laboratoire danalyse infor- matique de la parole (LAIP), Université de Lausanne, Switzerland
  24. Acharya Project by IIT, Madras. Multilingual computing fo r Literacy and Edu- cation, http://acharya.iitm.ac.in/disabilities/tts.php
  25. Ramani, S., Chandrasekar, R., Anjaneyulu, K.S.R. (eds.): KBCS 1989. LNCS, vol. 444. Springer, Heidelberg (1990)
  26. Sarkar, T., Keri, V., Yuvaraj, S., Prahalad, K.: Building Bengali Voice Using Fes- tival. In: Proceedings of ICLSI 2005, Hyderabad, India (2005)
  27. Kishore, S.P., Sangal, R., Srinvas, M.: Building Hindi and Telugu Voices using Festvox. In: Proceedings of International Conference on Natural Language Pro- cessing, ICON (2002)
  28. Raghavendra, E.V., Desai, S., Yegnanarayana, B., Black, A.W., Prahallad, K.: Global Syllable Set for Building Speech Synthesis in Indian Languages. In: Proceed- ings of IEEE workshop on Spoken Language Technologies, Goa, India (December 2008)
  29. About Sanskrit, http://www.sanskrit.nic.in/ABOUTSANSKRIT1.htm
  30. Ager, S.: Omniglot writing systems and languages of the world, http://www.omniglot.com/writing/devanagari.htm
  31. Veera Raghavendra, E., Prahallad, K.S.: Database Pruning for Indian Language Unit Selection Synthesizers. In: ICON-2009, Hyderabad, India (December 2009)
  32. Black, A.W.: CLUSTERGEN: a statistical parametric synthesizer using trajectory modeling. In: Proceedings of Interspeech, pp. 1762-1765 (2006)
  33. Black, A.W., Lenzo, K.: Building voices in the festival speech synthesis system (2000), www.festvox.org/festvox/index.html
  34. Tokuda, K., Yoshimura, T., Masuko, T., Kobayashi, T., Kitamura, T.: Speech pa- rameter generation algorithms for HMM-based speech. In: Proceedings of ICASSP, Istanbul, Turkey, pp. 1315-1318 (June 2000)
  35. Black, A.W., Bennett, C.L., Blanchard, B.C., Kominek, J., Langner, B., Prahallad, K., Toth, A.R.: CMU Blizzard 2007: A Hybrid Acoustic Unit Selection System from Statistically Predicted Parameters. In: Blizzard Challenge 2007, Bonn, Germany (2007)
  36. Jain, H., Kande, V., Desikan, K.: Vani -An India Language Text to speech Syn- thesizer. IIT, Mumbai
  37. Black, A.W., Taylor, P.: Automatically Clustering Similar Units for Unit Selection in Speech Synthesis (1997)
  38. Cardona, G.: Pān . ini: His Work and its Traditions. Vol. I, Background and Intro- duction, 2nd edn. Motilal Banarsidass, Delhi (1997)
  39. Goyal, P., Kulkarni, A., Behera, L.: Computer Simulation of As . t . ādhyāyi: Some In- sights. In: Huet, G., Kulkarni, A., Scharf, P. (eds.) Sanskrit Computational Linguis- tics 2007/2008. LNCS (LNAI), vol. 5402, pp. 139-161. Springer, Heidelberg (2009)
  40. Huet, G., Kulkarni, A., Scharf, P. (eds.): Sanskrit Computational Linguistics 2007/2008. LNCS (LNAI), vol. 5402. Springer, Heidelberg (2009)
  41. Hyman, M.: From Pān . inian Sandhi to Finite State Calculus. In: Huet, G., Kulkarni, A., Scharf, P. (eds.) Sanskrit Computational Linguistics 2007/2008. LNCS (LNAI), vol. 5402, pp. 253-265. Springer, Heidelberg (2009)
  42. Mishra, A.: Simulating the Pān . inian System of Sanskrit Grammar. In: Huet, G., Kulkarni, A., Scharf, P. (eds.) Sanskrit Computational Linguistics 2007/2008. LNCS (LNAI), vol. 5402, pp. 127-138. Springer, Heidelberg (2009)
  43. Scharf, P. M.: Rāmopākhyāna-the Story of Rāma in the Mahābhārata: An Independent-study Reader in Sanskrit. Routledge Curzon, London (2002)
  44. Scharf, P.: Modeling Pān . inian Grammar. In: Huet, G., Kulkarni, A., Scharf, P. (eds.) Sanskrit Computational Linguistics 2007/2008. LNCS (LNAI), vol. 5402, pp. 95-126. Springer, Heidelberg (2009)
  45. Scharf, P.: Rule selection in the As . t . ādhyāyi or Is Pān . ini's grammar mechanistic? In: Proceedings of the 14th World Sanskrit Conference, Kyoto University, Kyoto, September 1-5 (2009) (forthcoming)
  46. Scharf, P., Malcolm, H.: Linguistic issues in encoding Sanskrit. The Sanskrit Library; Providence. Delhi; Motilal Banarsidass (2010) References
  47. Bharati, A., Kulkarni, A.P., Sheeba, V.: Building a wide coverage Sanskrit Mor- phological Analyser: A practical approach. In: The First National Symposium on Modelling and Shallow Parsing of Indian Languages, IIT-Bombay (2006)
  48. Bhat, G.M.: Samāsah . . Samskrita Bharati, Bangalore, Karnataka (2006)
  49. Fortes, F.C.L., Roxas, R.E.O.: Optimality Theory in Morphological Analysis. In: National Natural Language Processing Research Symposium (January 2004)
  50. Fosler, J.E.: On Reversing the Generation Process in Optimality Theory. In: Pro- ceedings of the Association for Computational Linguistics (1996)
  51. Gillon, B.S.: Exocentric Compounds in Classical Sanskrit. In: Proceeding of the First International Symposium on Sanskrit Computational Linguistics (SCLS 2007), Paris, France (2007)
  52. Gillon, B.S.: Tagging Classical Sanskrit Compounds. In: Kulkarni, A., Huet, G. (eds.) Sanskrit Computational Linguistics. LNCS (LNAI), vol. 5406, pp. 98-105.
  53. Springer, Heidelberg (2009)
  54. Hellwig, O.: Sanskrit Tagger: A Stochastic Lexical and POS Tagger for Sanskrit. In: Huet, G., Kulkarni, A., Scharf, P. (eds.) Sanskrit Computational Linguistics 2007/2008. LNCS (LNAI), vol. 5402, pp. 266-277. Springer, Heidelberg (2009)
  55. Hellwig, O.: Extracting Dependency Trees from Sanskrit Texts. In: Kulkarni, A., Huet, G. (eds.) Sanskrit Computational Linguistics. LNCS (LNAI), vol. 5406, pp. 106-115. Springer, Heidelberg (2009)
  56. Hoeks, J.C.J., Hendriks, P.: Optimality Theory and Human Sentence Processing: The Case of Coordination. In: Proceedings of the 27th Annual Meeting of the Cognitive Science Society, Erlbaum, Mahwah, NJ, pp. 959-964 (2005)
  57. Huet, G.: Shallow syntax analysis in Sanskrit guided by semantic nets constraints. In: Proceedings of International Workshop on Research Issues in Digital Libraries, Kolkata (2006)
  58. Huet, G.: Lexicon-directed Segmentation and Tagging of Sanskrit. In: XIIth World Sanskrit Conference, Helsinki, Finland (August 2003);
  59. Tikkanen, B., Hettrich, H. (eds.) Themes and Tasks in Old and Middle Indo-Aryan Linguistics, Motilal Ba- narsidass, Delhi, pp. 307-325 (2006)
  60. Huet, G.: Formal structure of Sanskrit text: Requirements analysis for a Mechanical Sanskrit Processor. In: Huet, G., Kulkarni, A., Scharf, P. (eds.) Sanskrit Compu- tational Linguistics 2007/2008. LNCS (LNAI), vol. 5402, pp. 162-199. Springer, Heidelberg (2009)
  61. Huet, G.: Sanskrit Segmentation. In: South Asian Languages Analysis Roundtable XXVIII, Denton, Ohio (October 2009)
  62. Jha, G.N., Mishra, S.K.: Semantic Processing in Pān . ini's Kāraka System. In: Huet, G., Kulkarni, A., Scharf, P. (eds.) Sanskrit Computational Linguistics 2007/2008. LNCS (LNAI), vol. 5402, pp. 239-252. Springer, Heidelberg (2009)
  63. Kulkarni, A.P., Shukla, D.: Sanskrit Morphological Analyser: Some Issues. To ap- pear in Bh.K Festschrift volume by LSI (2009)
  64. Kulkarni, A.P., Kumar, A., Sheeba, V.: Sanskrit compound paraphrase generator. In: Proceedings of ICON 2009: 7th International Conference on Natural Language Processing. Macmillan Publishers, India (2009)
  65. Mahavira: Pādnini as Grammarian (With special reference to compound forma- tion). Bharatiya Vidya Prakashan, Delhi (June 1978)
  66. Mittal, V.: Automatic Sanskrit Segmentizer using Finite State Transducers. In: Proceeding of Association for Computational Linguistics -Student Research Work- shop (2010)
  67. Murty, M.S.: Sanskrit Compounds-A Philosophical Study. Chowkhamba Sanskrit Series Office, Varanasi, India (1974)
  68. Ishvarachandra, P.: As . tādhyāyī. Chaukhamba Sanskrit Pratisthan, Delhi (2004)
  69. Prince, A., Smolensky, P.: Optimality Theory: Constraint Interaction in Generative Grammar. In: RuCCS Technical Report 2 at Center for Cognitive Science, Rutgers University, Piscataway (1993)
  70. Sarma, E.R.S.: Man . ikan . a: A Navya-Nyāya Manual. The Adyar Library and re- search Centre, Madras (1960)
  71. Scharf, P.M.: Levels in Pān . ini's As . tādhyāyī. In: Kulkarni, A., Huet, G. (eds.) San- skrit Computational Linguistics. LNCS (LNAI), vol. 5406, pp. 66-77. Springer, Heidelberg (2009)
  72. Yuret, D., Biçici, E.: Modeling Morphologically Rich Languages Using Split Words and Unstructured Dependencies. In: ACL-IJCNLP, Singapore (2009) References
  73. Bharati, A., Sangal, R.: A Karaka Based Approach to Parsing of Indian Languages. In: COLING 1990: Proc. of Int. Conf. on Computational Linguistics, Helsinki, vol. 3. ACL, NY (August 1990)
  74. Bharati, A., Chaitanya, V., Sangal, R.: NLP A Paninian Perspective. Prentice Hall of India, Delhi (1994)
  75. Cardona, G.: Pān . ini and Pān . inīyas on śes . a Relations. Kunjunni Raja Academy of Indological Research, Kochi (2007)
  76. Dash, A.: The syntactic role of adhi in the Pāninian kāraka system. In: Deshpande, M.M., Bhate, S. (eds.) Paninian Studies Prof. S. D. Joshi Felicitation Volume, Center for South and Southeast Asian Studies, University of Michigan, U.S.A. (1991)
  77. Gent, I.P., Jefferson, C., Miguel, I.: MINION: A Fast, Scalable, Constraint Solver. In: The European Conference on Artificial Intelligence 2006, ECAI 2006 (2006)
  78. Gillon, B.S.: Word Order in Classical Sanskrit. Indian Linguistics 57(1), 1-35 (1996)
  79. Gillon, B.S.: Bhartr . hari's rule for unexpressed kārakas: The problem of control in Classical Sanskrit. In: Deshpande, H. (ed.) Indian Linguistic Studies, Festschrift in Honor of George Cardona, Motilal Banarasidass, Delhi (2002)
  80. Hellwig, O.: Extracting Dependency Trees from the Sanskrit Texts. In: Huet, G., Kulkarni, A., Scharf, P. (eds.) Sanskrit Computational Linguistics Symposium. Springer, Heidelberg (2009)
  81. Huet, G.: Formal Structure of Sanskrit Text: Requirements Analysis for a Mechan- ical Sanskrit Processor. In: Huet, G., Kulkarni, A., Scharf, P. (eds.) Sanskrit Com- putational Linguistics 2007/2008. LNCS (LNAI), vol. 5402, pp. 162-199. Springer, Heidelberg (2009)
  82. Huet, G.: Shallow syntax analysis in Sanskrit guided by semantic nets constraints. In: Majumder, Mitra, Parui (eds.) Proceedings of International Workshop on Re- search Issues in Digital Libraries, ACM Digital Library (December 2006)
  83. Jigyasu, B.: Ashtadhyayi (Bhashya) Prathamavrtti, Three volumes. Ramlal Kapoor Trust Bahalgadh, Sonepat, Haryana (1979) (in Hindi)
  84. Joshi, S.D. (ed.): Patanjali's Vyakarana Mahabhashya(several volumes). Univ. of Poona, Pune (1968)
  85. Joshi, S.D., Roodebergen, J.A.F.: The Aśt . ādhyāyīof Pān . ini (several volumes). Sahitya Akademi, Delhi (1998)
  86. Kiparsky, P.: On the Architecture of Panini's Grammar. In: Huet, G., Kulkarni, A., Scharf, P. (eds.) Sanskrit Computational Linguistics 2007/2008. LNCS (LNAI), vol. 5402, pp. 33-94. Springer, Heidelberg (2009)
  87. Kutumbashastri, V.: Sam . ks . epa Ramāyan . am. Teach Yourself Samskrit Series. Rashtriya Sanskrit Samsthanam, New Delhi (2002)
  88. Lin, D.: Dependency-based evaluation of MINIPAR. In: Workshop on the evalua- tion of Parsing Systems, Granada, Spain (1998)
  89. Marneffe, M., MacCartney, B., Manning, C.D.: Generating Typed Dependency Parses from Phrase Structure Parses. The Fifth International Conference on Lan- guage Resources and Evaluation, LREC 2006, Italy (2006)
  90. Pande, G.D.: Vaiyākaran . a Siddhāntakaumudī of Bhattojidikshita (Text only), Reprint Edition. Chowkhamba Vidyabhavan, Varanasi (2000)
  91. Ramakrishnamacharyulu, K.V.: Annotating Sanskrit Texts based on Śābdabodha systems. In: Huet, G., Kulkarni, A., Scharf, P. (eds.) Sanskrit Computational Lin- guistics 2007/2008. LNCS (LNAI), vol. 5402, Springer, Heidelberg (2009)
  92. Ramanujatatacharya, N.S.: Śābdabodha Mīmām . sā. Institute Francis De Pondicherry (2005)
  93. Sharma, R.: Vākyapadīyam, Part III With commentary Prakāśa by Helaraja and Ambakartri. Varanaseya Sanskrit Visvavidyalaya, Varanasi (1974)
  94. SK: Siddhāntakaumudī See Pande
  95. Staal, J.F.: Word Order in Sanskrit and Universal Grammar. In: Foundations of Language. Supplementary Series, vol. 5. Reidal, Dordercht (1967)
  96. Sleator, D.D., Temperley, D.: Parsing English with a link grammar. In: Third international Workshop on Parsing Technologies (1993) References
  97. BFO (Basic Formal Ontology), http://www.ifomis.org/bfo
  98. GFO (General Formal Ontology), http://www.onto-med.de/ontologies/gfo/
  99. OCHRE (Object-Centered High-level REference Ontology), http://ifomis.uni-leipzig.de/
  100. DOLCE (Descriptive Ontology for Linguistic and Cognitive Engineering), http://www.loa-cnr.it/DOLCE.html
  101. Kan . āda: The Vaiśes . ika sutras of Kan . āda, with the commentary of Śamkara Misra and extracts from the gloss of Jayanarayana. Translated in English by Nan- dalal Sinha. Allahabad (1911); 2nd edn. Revised and enlarged, Allahabad (1923);
  102. Reprinted New York (1974), Delhi (1986)
  103. Praśastapāda: Padārthadharmasam . graha with Nyāyakandalī of Sridhara. English Translation by Ganganatha Jha. Chowkhamba, Varanasi (Reprint 1982)
  104. Udayana: Lakshan . āvali. In Musashi Tachikawa: The Structure of the World in Udayana's Realism: A study of the Lakshan . āvali and Kiran . āvali. Springer, Heidel- berg (1982)
  105. Singh, N.: Comprehensive Schema of Entities Vaiśesika Category System. Science Philosophy Interface 5(2), 1-54 (2001)
  106. Singh, N.: Formal Theory of Categories through the Logic of Punctuator (2002) (unpublished)
  107. Singh, N.: Theory of Experiential Contiguum. Philosophy and Science Exploratory Approach to Consciousness, pp. 111-159. Ramakrishna Mission Institute of Cul- ture, Kolkata (2003)
  108. Singh, N.: Foundations of Ontological Engineering. Lecture slides of course at IIIT Hyderabad (2008)
  109. Rozenberg, G., et al. (eds.): Foundations. Handbook of Graph Grammars and Computing by Graph Transformation, vol. 1. World Scientific, Singapore (1997)
  110. The Attributed Graph Grammar System, http://user.cs.tu-berlin.de/ ~gragra/agg/ References
  111. Bharati, A., Chaitanya, V., Sangal, R.: Natural Language Processing: A Paninian Perspective. Prentice Hall of India, New Delhi (1995)
  112. Bhattacharya, G.: Vis . ayatāvāda. Prasad, Varanasi (1943)
  113. Carnie, A.: Syntax A Generative Introduction. Blackwell Publishing, Oxford (2006)
  114. Corbett, G.G., Fraser, N.M., Scott, M. (eds.): Heads in Grammatical Theory. Cam- bridge University Press, Cambridge (1993)
  115. Culicover, P.W.: Principles and Parameters: An Introduction to Syntactic theory. Oxford University Press, New York (2005)
  116. Jha, G.N., Sobha, L., Diwakar, M., Surjit, K.S., Praveen, P.: Anaphors in Sanskrit. In: Johansson, C. (ed.) Proceedings of the Second Workshop on Anaphora Resolu- tion, vol. 2. Cambridge Scholars Publishing, Cambridge (2008) ISSN 1736-6305
  117. Jhalkikar, B.: Nyāyakośa. Bhandarakar Oriental Research Institute, Pune (1893)
  118. Kübler, S., McDonald, R., Nivre, J.: Dependency Parsing. Morgan and Claypool Publishers, San Francisco (2009)
  119. Mahalakshmi, G.S., Geetha, T.V., Kumar, A., Kumar, D., Manikandan, S.: Gau- tama -Ontology Editor Based on Nyaya Logic. In: Ramanujam, R., Sarukkai, S. (eds.) Logic and Its Applications. LNCS (LNAI), vol. 5378, pp. 232-242. Springer, Heidelberg (2009)
  120. Mahalakshmi, G.S., Geetha, T.V.: Representing Knowledge Effectively Using In- dian Logic. In: Sajja, A. (ed.) TMRF e-book, Advanced Knowledge based Systems, Model, Applications, Research, vol. I, pp. 12-28 (2010), http://www.tmrfindia.org/eseries/ebookV1-C2.pdf
  121. Pedersen, M., Domenyk, E., Amin, S.K., Lakshmi P.: Relative clauses in Hindi and Arabic: a Paninian dependency grammar analysis. In: Coling 2004 workshop: Proceedings Recent Advances in Dependency Grammar. Prentice Hall of India Pvt. Ltd., Geneva (2004)
  122. Potter, K.: Encyclopedia of Indian Philosophy. Motilal Banarasidas, Delhi (2000)
  123. Sarma, V.V.S.: Survey of Indian Logic from the point of view of Computer Science. In: Sadhana, vol. 19 pp.196971-196983 (1994), http://www.springerlink.com/content/lnv6656gg37022t0
  124. Shastri, G.: Patañjali's Vyākaran . amahābhās . ya with Kaiyat . a's Pradīpa and Bhat . t . oji Dīks . ita's śabdakaustubha and Nāgeśabhat . t . a's Udyota with commentary Rājalaks . mī. Rashtriya Sanskrit Sansthan, New Delhi (Reprint 2006)
  125. Smith, N.: Chomsky Ideas and Ideals. Cambridge University Press, New York (2004)
  126. Bhagavat, P.V.B.: (Tr.(in Marathi)): Paramalaghumañjus . . Paramarsha Prakashan, Pune Vidyapith Tattvajnana Vibhaga, Pune (1984)
  127. Mahoney [
  128. sorts some Sanskrit texts but as far as we know, no one has sorted the entire Mahābhārata. We have sorted the Mahābhārata to reduce the search size for exact matching. A previously unavailable sorted verse-index of the Mahābhārata has thus been created as part of this effort. See the URL http://www.iiitb.ac.in/faculty/srao/other-information/ for a link to the same.
  129. See [ [12] ] for more information on the Mahābhārata-Tātparyanirn . aya and its author.
  130. In this regard, see [ [12] ] for some general discussion. Mesquita claims [ [7] ] that Mad- hva's citations are his own inventions, a charge countered by Rao and Sharma [ [10] ].
  131. Mesquita recently has repeated [ [8] ] the charge in the specific context of Madhva's citations from the Mahābhārata.
  132. Related Work Citation matching is one of the active areas of research. Citation matching is used for identifying authors of scientific papers [
  133. 5] ], knowledge discovery, etc. sort, merge sort, etc. 4 There is an x86 assembly language program [ [16] ] which sorts Sanskrit texts. Mahoney [
  134. specifies the maximum text size that these programs can handle. Also as far as our knowledge goes, nobody has sorted the entire Mahābhārata text. Gale and Church [
  135. describe a method for aligning sentences based on a sample statisti- cal model of character length. This method is used by Csernel and Patte [ [2] ] for performing comparisons between Sanskrit manuscripts. Local alignment is one of the sequence alignment techniques used in bioin- formatics to find the similarity regions between two DNA, RNA or protein se- quences of unequal length. The Smith-Waterman algorithm [ [14] ] is used widely for performing local alignment. The Smith-Waterman algorithm is more accurate but computationally intensive as compared to other methods like BLAST. The Smith-Waterman-Gotoh algorithm [
  136. is an open source similarity measurement library implemented in Java. Basic algorithms like edit distance and Smith-Waterman-Gotoh [ [4] ] are implemented in this library, which we have used.
  137. Problem Definition Citation matching is an unexplored area in natural language texts, especially in Sanskrit literature. Table 1 shows examples of sources and citations. Citation matching in Sanskrit literature is complex compared to the same problem with scientific literature because of the following reasons. References
  138. Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 2nd edn. MIT Press/McGraw-Hill (2001)
  139. Csernel, M., Patte, F.: Critical edition of Sanskrit texts. In: Huet, G., Kulkarni, A., Scharf, P. (eds.) Sanskrit Computational Linguistics 2007/2008. LNCS (LNAI), vol. 5402, pp. 358-379. Springer, Heidelberg (2009)
  140. Gale, W.A., Church, K.W.: A program for aligning sentences in bilingual corpora. In: Meeting of the Association for Computational Linguistics, pp. 177-184 (1991)
  141. Gotoh, O.: An improved algorithm for matching biological sequences. Journal of Molecular Biology 162(3), 705-708 (1982)
  142. Lawrence, S., Bollacker, K., Giles, L.C.: Autonomous citation matching. In: Etzioni, O. (ed.) Proceedings of the Third International Conference on Autonomous Agents. ACM Press, New York (1999)
  143. Mahoney, R.: Arbitrary lexicographic sorting: Sort UTF-8 encoded Romanised Sanskrit, http://www.indica-et-buddhica.org/sections/repositorium-preview/ materials/software/sort-utf8-sanskrit
  144. Mesquita, R.: Madhva's Unknown Literary Sources: Some Observations. Aditya Prakashan, New Delhi (2000)
  145. Mesquita, R.: Madhva's Quotes from the Puranas and the Mahabharata: An An- alytical Compilation of Untraceable Source-Quotations in Madhva's Works along with Footnotes. Aditya Prakashan, New Delhi (January 2008)
  146. Pasula, H., Marthi, B., Milch, B., Russell, S., Shpitser, I.: Identity uncertainty and citation matching (2002)
  147. Rao, S., Sharma, B.N.K.: Madhva's unknown sources: A review. Asiatische Stu- dien Études Asiatiques LVII 1, 181-194 (2003)
  148. Robinson, P.: The one text and the many texts. Literary and Linguistic Comput- ing 15(1), 5-14 (2000)
  149. Sharma, B.N.K.: History of the Dvaita School of Vedanta and its Literature, 3rd edn. Motilal Banarsidass, Delhi (2000)
  150. Smith, J.: The Mahabharata (2009), http://bombay.indology.info/mahabharata/statement.html
  151. Smith, T.F., Waterman, M.S.: Identification of common molecular subsequences. Journal of Molecular Biology 147(1), 195-197 (1981)
  152. Su, Z., Ahn, B.R., Eom, K.Y., Kang, M.K., Kim, J.P., Kim, M.K.: Plagiarism detection using the Levenshtein Distance and Smith-Waterman algorithm. In: In- ternational Conference on Innovative Computing, Information and Control. IEEE Computer Society, Los Alamitos (2008)
  153. Dudenskt, http://www.sanskritweb.net/koko/dudenskt.pdf
  154. Chapman, S.: SimMetrics -open source Similarity Measure Library, http://www.dcs.shef.ac.uk/ ~sam/simmetrics.html
  155. Bhise, U.R.: Nāradīyā Śiks . ā with the Commentary of Bhat . t . a Śobhākara. Bhan- darkar Oriental Research Institute, Poona (1986)
  156. von Böhtlingk, O.: Pān . ini's Grammatik (Primary source text for our database). Olms, Hildesheim (1887)
  157. Bronkhorst, J.: Greater Magadha Studies in the Culture of Early India. Brill, Leiden (2007)
  158. Dhūpakara, A. Ś.: Śrīpi ṅgalanāga-viracitam . Chandah . śāstram. Parimal Publica- tions, Delhi (1985)
  159. Deshpande, M.M.: Semantics of Kārakas in Pān . ini: An Exploration of Philosoph- ical and Linguistical Issues. In: Matilal, B.K., Bilimoria, P. (eds.) Sanskrit and Related Studies: Contemporary Researches and Reflections, pp. 33-57. Sri Sat- guru Publications, Delhi (1990)
  160. Gladigow, B.: Sequenzierung von Riten und die Ordnung der Rituale. In: Stausberg, M. (ed.) Zorostrian Rituals in Context, Numen Book Series, Studies in the History of Religions, vol. CII, pp. 57-76. Brill, Boston (1999)
  161. Joshi, S.D., Roodbergen, J.A.F.: On siddha, asiddha and sthānivat, vol. LXVIII, pp. 541-549. Annals of the Bhandarkar Oriental Research Institute, Poona (1987)
  162. Joshi, S.D., Roodbergen, J.A.F.: The As . t . ādhyāyī of Pān . ini, with Translation and Explanatory Notes, vol. II. Sahitya Akademi, New Delhi (1993)
  163. Houben, J.E.M.: 'Meaning statements' in Pān . ini's grammar: on the purpose and context of the As . t . ādhyāyī. Studien zur Indologie und Iranistik 22, 23-54 (1999- 2001)
  164. Houben, J.E.M.: Memetics of Vedic Ritual, Morphology of the Agnis . t . oma. In: Griffiths, A., Houben, J.E.M. (eds.) The Vedas: texts, language & ritual, pp. 23- 47.
  165. Forsten, Groningen (2004)
  166. Katre, S.M.: As . t . ādhyāyī of Pān . ini. Motilal Banarsidass, Delhi (1989)
  167. Lüders, H.: Die Vyâsa-C ¸ikshâ besonders in ihrem Verhältnis zum Taittirîya- Prâtiçâkhya, Göttingen (1894)
  168. Michaels, A.: 'Le rituel pour le rituel' oder wie sinnlos sind Rituale? In: Caduff, C., Pfaff-Czarnecka, J. (eds.) Rituale heute: Theorien -Kontroversen -Entwürfe, pp. 23-47. Reimer, Berlin (1999)
  169. Michaels, A.: The Grammar of Rituals. In: Michaels, A., Mishra, A. (eds.) Gram- mar and Morphology of Ritual, Ritual Dynamics and the Science of Ritual, vol. I, pp. 15-36. Harrassowitz Verlag, Wiesbaden (2010)
  170. Mishra, A.: Simulating the Pān . inian System of Sanskrit Grammar. In: Huet, G., Kulkarni, A., Scharf, P. (eds.) Sanskrit Computational Linguistics 2007/2008. LNCS (LNAI), vol. 5402, pp. 127-138. Springer, Heidelberg (2009)
  171. Mishra, A.: Modelling the Grammatical Circle of the Pān . inian System. In: Huet, G., Kulkarni, A., Scharf, P. (eds.) Sanskrit Computational Linguistics 2007/2008. LNCS (LNAI), vol. 5402, pp. 40-55. Springer, Heidelberg (2009)
  172. Mishra, A.: On the Possibilities of a Pān . inian Paradigm for a Rule-based Descrip- tion of Rituals. In: Michaels, A., Mishra, A. (eds.) Grammar and Morphology of Ritual, Ritual Dynamics and the Science of Ritual, vol. I, pp. 15-36. Harrassowitz Verlag, Wiesbaden (2010)
  173. Sarup, L.: The Nighan . t . u and The Nirukta. Motilal Banarsidass, Delhi (1967)
  174. Oppitz, M.: Montageplan von Ritualen. In: Caduff, C., Pfaff-Czarnecka, J. (eds.) Rituale heute: Theorien -Kontroversen -Entwürfe, pp. 23-47. Reimer, Berlin (1999)
  175. Sarup, L.: The Nighan . t . u and the Nirukta. Motilal Banarasidass, Delhi (1967)
  176. Shastri, K.: āgeśabhat . t . a-kr . ta Vaiyākaran . a-siddhānta-parama-laghu-mañjūs . ā. Ku- rukshetra University Press, Kurukshetra (1975)
  177. Shastri, M.D.: The R . gveda-Prātiśākhya with the commentary of Uvat . a. Vaidika Svadhyaya Mandir, Varanasi (1959)
  178. Scharf, P.M.: Modeling Pān . inian Grammar. In: Huet, G., Kulkarni, A., Scharf, P. (eds.) Sanskrit Computational Linguistics 2007/2008. LNCS (LNAI), vol. 5402, pp. 95-126. Springer, Heidelberg (2009)
  179. Scharfe, H.: A new perspective on Pān . ini. In: Indologica Taurinensia, vol. XII, pp. 1-273 (2009)
  180. Staal, F.: Rules without meaning: ritual, mantras and the human sciences. Lang, New York (1989)
  181. Thieme, P.: Meaning and form of the 'grammar' of Pān . ini. Studien zur Indologie und Iranistik 8/9, 12-22 (1982)
  182. Varma, V.K. (ed.): Vājasaneyi-Prātiśākhyam. Chaukhamba Sanskrit Pratishthan, Delhi (1987)
  183. Varma, V.K.: R . gveda-Prātiśākhyam. Banaras Hindu University Sanskrit Series, Varanasi (1972)
  184. Wezler, A.: On the quadruple division of the Yogaśāstra, the caturvyūhatva of the Cikitsāśāstra and the 'four noble truths' of the Buddha. In: Indologica Taurinensia, vol. XII, pp. 289-337 (1984)
  185. Baskaran, S., Bali, K., Bhattacharya, T., Bhattacharyya, P., Choudhury, M., Jha, G.N., Rajendran, S., Saravanan, K., Sobha, L., Subbarao, K.V.S.: A Common Parts-of-Speech Tagset Framework for Indian Languages. In: LREC, Marrakech, Morocco (2008)
  186. Baskaran, S., et al.: Framework for a Common Parts-of-Speech Tagset for Indi- cLanguages (2007), http://research.microsoft.com/ ~baskaran/POSTagset
  187. Cardona, G.: Pān . ini: His work and its traditions. Motilal Banarasidass, Delhi (1988)
  188. Chandrashekar, R.: Parts-of-Speech Tagging For Sanskrit. Ph.D. thesis submitted to JNU, New Delhi (2007)
  189. Greene, B.B., Rubin, G.M.: Automatic grammatical tagging of English. Depart- ment of Linguistics, Brown University, Providence, R.I. (1981)
  190. Hardie, A.: The Computational Analysis of Morphosyntactic Categories in Urdu. PhD Thesis submitted to Lancaster University (2004)
  191. Hellwig, O.: SANSKRITTAGGER, A Stochastic Lexical and POS Tagger for San- skrit. In: Huet, G., Kulkarni, A. (eds.) Sanskrit Computational Linguistics 2007. LNCS (LNAI), vol. 5402. Springer, Heidelberg (2009)
  192. Huet, G.: The Sanskrit Heritage Site, http://sanskrit.inria.fr/
  193. IIIT-Tagset. A Parts-of-Speech tagset for Indian Languages, http://shiva.iiit.ac.in/SPSAL2007/iiit_tagset_guidelines.pdf
  194. Jha, G.N.: Generating nominal inflectional morphology in Sanskrit. In: SIMPLE 2004, IIT-Kharagpur Lecture Compendium, Shyama Printing Works, Kharagpur (2004)
  195. Jha, G.N., Gopal, M., Mishra, D.: Annotating Sanskrit Corpus: adapting IL- POSTS. In: Vetulani, Z. (ed.) Proceedings of the 4th Language and Technology Conference: Human Language Technologies as a Challenge for Computer Science and Linguistics, pp. 467-471 (2009)
  196. Jha, G.N., Mishra, S.: Semantic processing in Panini's karaka system. In: Huet, G., Kulkarni, A., Scharf, P. (eds.) Sanskrit Computational Linguistics 2007/2008. LNCS (LNAI), vol. 5402. Springer, Heidelberg (2009)
  197. Kale, M.R.: A Higher Sanskrit Grammar. MLBD Publishers, New Delhi (1995)
  198. Leech, G., Wilson, A.: Recommendations for the Morphosyntactic Annotation of Corpora. EAGLES Report EAG-TCWG-MAC/R (1996)
  199. Leech, G., Wilson, A.: Standards for Tag-sets. In: van Halteren, H. (ed.) Syntactic Word class Tagging. Kluwer Academic, Dordrecht (1999)
  200. Leech, G.: Grammatical Tagging. In: Garsire, Leech, McEnery (eds.) Corpus Anno- tation: Linguistic Information for Computer Text Corpora. Longman, London (1997)
  201. Mishra, S., Jha, G.N.: Identifying verb inflections in Sanskrit morphology. In: Pro- ceedings of SIMPLE 2004, IIT Kharagpur (2005)
  202. Ramkrishnamacharyulu, K.V.: Annotating Sanskrit Texts Based on Sabdabodha Systems. In: Kulkarni, A., Huet, G. (eds.) Sanskrit Computational Linguistics. LNCS (LNAI), vol. 5406, pp. 26-39. Springer, Heidelberg (2009)
  203. Rishi, U.S.S. (ed.): Yaska-pranitam niruktam, vol. I. Chowkhamba Vidyabhawan, Varanasi (2005)
  204. Santorini, B.: Part-of-speech tagging guidelines for the Penn Treebank Project. Technical report MS-CIS-90-47, Dept. Of Computer and Information Science, Uni- versity of Pennsylvania (1990) References
  205. Csernel, M., Patte, F.: Critical edition of Sanskrit texts. In: Huet, G., Kulkarni, A., Scharf, P. (eds.) Sanskrit Computational Linguistics 2007/2008. LNCS (LNAI), vol. 5402, pp. 358-379. Springer, Heidelberg (2009)
  206. Halácsy, P., Kornai, A., Oravecz, C., Trón, V., Varga, D.: Using a morphological analyzer in high precision POS tagging of Hungarian. In: Proceedings of 5th Con- ference on Language Resources and Evaluation (LREC), Citeseer, pp. 2245-2248 (2006)
  207. Haque, W., Aravind, A., Reddy, B.: Pairwise sequence alignment algorithms: a sur- vey. In: Proceedings of the 2009 conference on Information Science, Technology and Applications, pp. 96-103 (2009)
  208. Hellwig, O.: SanskritTagger, a stochastic lexical and POS tagger for Sanskrit. In: Huet, G., Kulkarni, A., Scharf, P. (eds.) Sanskrit Computational Linguistics 2007/2008. LNCS (LNAI), vol. 5402, pp. 266-277. Springer, Heidelberg (2009)
  209. Leech, G., Garside, R., Bryant, M.: CLAWS4: The tagging of the British National Corpus. In: Proceedings of the 15th International Conference on Computational Linguistics, Kyoto pp. 622-628 (1994)
  210. Marcus, M.P., Marcinkiewicz, M.A., Santorini, B.: Building a large annotated corpus of English: The Penn Treebank. Computational Linguistics 19(2), 313-330 (1993)
  211. Megyesi, B.: Improving Brill's POS tagger for an agglutinative language, pp. 275- 284.
  212. Sproat, R., Shih, C.: Corpus-based methods in Chinese morphology and phonology. In: Proceedings of the 19th International Conference on Computational Linguistics (2002)
  213. Weischedel, R., Schwartz, R., Palmucci, J., Meteer, M., Ramshaw, L.: Coping with ambiguity and unknown words through probabilistic models. Computational Lin- guistics 19(2), 360-382 (1993)
  214. 9 daivatāni pum . si vā (1.1.9) 10 nāsatyāvaśvinau dasrāvāśvineyau ca tāvubhau (1.1.51) 11 gr . hāh . pum . si ca bhūmnyeva (2.2.5) 12 klībāvyayam . tvapadiśam (1.3.5) References
  215. Bharati, A., Kulkarni, A., Nair, S.S.: 'Use of Amarakosha and Hindi WordNet in Building a Network of Sanskrit Words. In: International Conference On Natural Language Processing, C-DAC Pune (2008)
  216. Colebrooke, H.T.: Kosha or Dictionary of the sungskrita language by Umura singha with an English interpretations and annotations. Serampore (1808)
  217. Fellbaum, C.: WordNet An Electronic Lexical Database. MIT Press, Massachusetts (1999)
  218. Jha, G.N., Chandrashekar, R., Singh, U.K., Jha, V.N., Pandey, S., Singh, S.K., Mishra, M.K.: Online Multilingual Amarakośa: The Relational Lexical Database. In: 5th Global Wordnet Conference, IIT Mumbai (2010)
  219. Kulkarni, M., Pushpak, B.: Verbal roots in the Sanskrit WordNet. In: 2nd Interna- tional Sanskrit Computational Linguistics Symposium, Brown University (2008)
  220. Kulkarni, M., Dangarikar, C., Kulkarni, I., Nanda, A., Bhattacharya, P.: Introduc- ing Sanskrit Wordnet. In: 5th Global Wordnet Conference, IIT Mumbai (2010)
  221. Moosath, P.T.C.: Paarameswarii: malayalam commentory of Amarakosha. National Book Stall, Kottayam (1914)
  222. Moosath, P.T.C.: Triveni: malayalam commentory of Amarakosha. National Book Stall, Kottayam (1956)
  223. Nair, S.S., Swain, P., Kulkarni, A.: Developing network of Sanskrit words across Part-Of-Speech categories. In: CSATS 2009, Rashtriya Sanskrit Vidhyapeeth, Tiru- pati (2009)
  224. Oka, K.G.: The Namalinganushasana: Amarakosa of Amarasimha with the com- mentary (Amarakoshodghaatana) of Ksheraswamin. Upasana prakashan Delhi, Varanasi (1981)
  225. Online Multilingual Amarakosha: http://sanskrit.jnu.ac.in/amara/index.jsp
  226. Patkar, M.M.: History of Sanskrit Lexicography. Munshiram Manoharlal Publish- ers Pvt. Ltd., Delhi (1981)
  227. Pandit, S.: Namalinganusasana (Amarakosha) With the commentary of (Vyakhya- sudha or Ramasrami) of Bhanuji Dikshit. Tukaram javaji proprietor of the "Nir- naya Sagar" Press, Bombay (1915)
  228. Tiwari, D.: Devakosah . ardhāt amarakosah . . Nag prakashak, Delhi (1989)
  229. Wood, A.: Knowledge before printing and after -The Indian tradition in changing Kerala. Oxford University Press, Delhi (1985) References
  230. Agirre, E., German, R.: Word sense disambiguation using conceptual density. In: Proceedings of the 16th International Conference on Computational Linguistics, COLING (1996)
  231. Alshawi, H.: Processing dictionary definitions with phrasal pattern hierarchies. Computational Lingustics 13, 195-202 (1987)
  232. Bhattacharyya, P.: Indowordnet. In: Lexical Resources Engineering Conference, Malta (May 2010)
  233. Kulkarni, M., Dangarikar, C., Kulkarni, I., Nanda, A.P.B.: Introducing sanskrit wordnet. In: Bhattacharyya, P., Fellbaum, C., Vossen, P. (eds.) Proceedings on the 5th Global Wordnet Conference ( GWC 2010), Narosa,Mumbai, pp. 287-294 (2010)
  234. Lesk, M.: Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone. In: Proceedings of the 5th annual international conference on Systems documentation, Toronto, Ontario, Canada, pp. 24-26 (1986)
  235. Mihalcea, R.: Unsupervised large-vocabulary word sense disambiguation with graph-based algorithms for sequence data labeling. In: HLT/EMNLP 2005, Hu- man Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, Vancouver, British Columbia, Canada, October,6-8 (2005)
  236. Narayan, D., Chakrabarty, D., Pande, P., Bhattacharyya, P.: An experience in building the indo wordnet-a wordnet for hindi. In: International Conference on Global WordNet (GWC 2002), Mysore, India (2002)
  237. Resnik, P.: Selectional preference and sense disambiguation. In: Tagging Text with Lexical Semantics: Why, What, and How? In: Proceedings of ACL SIGLEX Work- shop on Tagging Text with Lexical Semantics: Why, What, and How?, pp. 52-57. ACL (1997)
  238. Sinha, M., Reddy, M., Bhattacharyya, P.: An approach towards construction and application of multilingual indo-wordnet. In: 3rd Global Wordnet Conference (GWC 2006), Jeju Island, Korea (2006)
  239. Walker, D., Amsler, R.: Analyzing Language in Restricted Domains. In: The Use of Machine Readable Dictionaries in Sublanguage Analysis, pp. 69-83. LEA Press (1986) References
  240. Ramshastri, A.: Sa ṁskr . ta śiks . an . a saran . ī. Acharya Ramshastri Jñānapīt . ha (Hindi) (1998)
  241. Cardona, G.: Pān . ini and Pān . inīyas on śes . a Relations. Kunjunni Raja Academy of Indological Research, Kochi (2007)
  242. Dorr, B.: Classification of Machine Translation Divergence and a Proposed Solu- tion. Computational Linguistics 20(4) (1994)
  243. Dave, S., Parikh, J., Bhattacharya, P.: Interlingua Based English-Hindi Machine Translation and Language Divergence. Journal of Machine Translation (JMT) 17 (2002)
  244. Dwivedi, K.D.: Bhās . ā vijñāna evam Bhās . ā śāstra. Viśvavidyālaya Prakāśana, Varanasi (2006)
  245. Goyal, P., Sinha, R., Mahesh, K.: Translation Divergence in English-Sanskrit-Hindi Language Pairs. In: Kulkarni, A., Huet, G. (eds.) SCLS 2009. LNCS (LNAI), vol. 5406, Springer, Heidelberg (2009)
  246. Mishra, V., Mishra, R.B.: Study of Example Based English to Sanskrit Machine Translation. Journal of Research and Development in Comp Sc. And Engg. (37) (January-June 2008)
  247. Sharma, R.R., Sharma, M.: Vaiyākaran . a Siddhānta Kaumudī of Śrī Bhat . t . oji Dīks . ita (Kāraka Prakaran . am). Bharatiya Vidya Prakashan, Varanasi-Delhi (Hindi) (1997)
  248. Sharma, R.N.: The As . t . ādhyāyī of Pān . ini, 2nd edn., vol. 1-3. Munshiram Manohar- lal, Delhi (2002)
  249. Vasu, Ś.C.: The Siddhānta Kaumudī of Bhat . t . oji Dīks . ita, vol. I. Motilal Banarsi- dass, Delhi (2003) References
  250. Byron, D., James, A.: Applying Genetic Algorithms to Pronoun Resolution. In: Proceedings of the Sixteenth National Conference on Artificial Intelligence AAAI (1999)
  251. Carbonell, J., Brown, R.: Anaphora Resolution: A Multistrategy Approach. In: Proceedings of the 12th International Conference on Computational Linguistics, pp. 96-101 (1988)
  252. Hobbs, J.: Resolving pronoun references. Lingua 44, 311-338 (1978)
  253. Hock, H.H.: Studies in Sanskrit Syntax. Motilal Banarasidas, Delhi (1991)
  254. Jha, G.N., Sobha, L., Mishra, D., Singh, S.K., Pralayankar, P.: Anaphors in San- skrit. In: Proceedings of the Second Workshop on Anaphora Resoplution (WAR-II). Nealt Proceedings Series, vol. 2, pp. 11-25 (2008)
  255. Jha, G.N., Sobha, L., Mishra, D.: Discourse Anaphor and Resolution Techniques in Sanskrit. In: Proceedings of the 7th Discourse Anaphora and Anaphora Resolution Colloquium, Goa, pp. 135-150 (2009)
  256. Kennedy, C., Boguraev, B.: Anaphora for Everyone: Pronominal Anaphora Reso- lution without a Parser. In: Proceedings of the 16th International Conference on Computational Linguistics (COLING 1996), Denmark, pp. 113-118 (1996)
  257. Lappin, S., Mccord, M.: Anaphora Resolution in Slot grammar. Computational Linguistics 16(4), 197-210 (1990)
  258. Lappin, S., Leass, H.: An Algorithm for Pronominal Anaphora Resolution. Com- putational Linguistics 20(4), 535-561 (1994)
  259. Mitkov, R.: Factors in Anaphora Resolution: They are not the only Things That Matter. A Case Study Based on Two Different Approaches. In: Proceedings of the ACL 1997/EACL 1997 Workshop on Operational Factors in Practical, Robust Anaphora Resolution, Spain, pp. 14-21 (1997)
  260. Sobha, L., Patnaik, B.N.: An Algorithm for Pronoun and Reflexive Resolution in Malayalam. In: International Conference on Computational Linguistics, Speech and Document processing, pp. 63-66 (1998)
  261. Sobha, L., Patnaik, B.N.: A Quantitative and a Non-Quantitative Approach to Resolve Reflexives and Pronouns in Malayalam. IJDL 27(1), 33-40 (1999)
  262. Sobha, L.: An Anaphora Resolution System for Malayalam and Hindi (An unpub- lished doctoral thesis) Mahatma Gandhi University, Kottayam (1999)
  263. Sobha, L., Pralayankar, P.: Anaphors in Sanskrit. In: International Conference on Referential Entity Resolution. AU-KBC, Chennai (2007)
  264. Viswanathan, S., Ramesh Kumar, S., Kumara Shanmugam, B., Arulmozi, S.: A Tamil Morphological analyser. In: Proceedings of International Conference on Nat- ural Language Processing, pp. 31-39 (2003) References
  265. Apte, V.S.: The student's guide to Sanskrit composition. A treatise on Sanskrit syntax for use of schools and colleges. Lokasamgraha Press, Poona (1960)
  266. Bronkhorst, J.: Three problems pertaining to the Mahābhās . ya. Bhandarkar Oriental Research Institute, Pune (1987)
  267. Gnoli, R. (ed.): The Pramān . avārttikam of Dharmakīrti, the first chapter with the autocommentary, text and critical notes. Istituto Italiano per il Medio ed Estremo Oriente, Rome (1960)
  268. Kielhorn, F.: Mahābhās . ya. 4th edition revised by R. N. Dandekar, 4th edn. Bhan- darkar Oriental Research Institute, Pune (1985)
  269. Peterson, P. (ed.): Kādambarī. Government Central Book Depot, Bombay (1885)
  270. Quirk, R., Greenbaum, S., Leech, G., Svartik, J.: A Comprehensive Grammar of the English Language. Longman, London (1985)
  271. Sā ṅkr . tyāyana, R. (ed): Ācārya-Dhamrakīrteh . Pramān . avārttikam (svārthānumāna- paricchedah . svopajñavr . ttyā, Karn . akagominviracitayā tat . t . īkayā ca sahitam. Kitab Mahal, Allahabad (1943)
  272. Scharf, P.: The denotation of generic terms in ancient Indian philosophy: grammar, Nyāya, and Mīmām . sā. American Philosophical Society, Philadelphia (1996) References
  273. Giri, S.P., Satyanarayanashastri.: Pan . inīyah . As . t . ādhyāyī. Krishnadas Academy, Varanasi (1984)
  274. Bhargava Sastri, B.J.: The Vyākaran . amahābhās . ya of Patañjali. Chaukhamba San- skrit Pratishtan, Delhi (1872)
  275. Kielhorn, F.: Paribhās . enduśekhara of Nāgojibhat . t . a. Parimal Publications, Delhi
  276. Mishra, S.: Kāśikā. Chaukamba Samskrita Samsthan, Varanasi (1979)
  277. Abhyankar, K.V.: Paribhashasamgraha. Bhandarkar Research Instt, Puna (1967)
  278. Kak, S.: The Paninian approach to natural language processing. International Jour- nal of Approximate Reasoning 1, 117-130 (1987)
  279. Kiparsky, P.: On the Architecture of Panini's Grammar
  280. Bhate, S., Kak, S.: Panini's Grammar and Computer Science
  281. Vasu, S.C.: The As . t . ādhyāyī of Pān . ini. Motilal Banarasidas Publishers, New Delhi (2003)
  282. Vasu, S.C.: Siddhānta Kaumudi, Motilal Banarasidas Publishers, New Delhi (2002)
  283. Cardona, G.: pūrvatrāsiddham and āsrayātsiddham, MS
  284. Sridhar, S., Srinivasa, V.: Computational Structure of As . t . ādhyāyī and Conflict Resolution Techniques. In: Kulkarni, A., Huet, G. (eds.) Sanskrit Computational Linguistics. LNCS (LNAI), vol. 5406. Springer, Heidelberg (2009)
  285. 6 padaprakr . tih . sam . hitā. padaprakr . tīni sarvacaran . ānām . pārs . adāni. (Ni. 1.17).
  286. padānām . yā prakr . tih . seyam . padaprakr . tih . . kim . kāran . am? sam . hitāto hi padāni prakriyante. tasmātsam . hitaiva prakr . tirvikārah . padānītyevameke manyante. apare punah . padaprakr . tih . sam . hiteti padāni prakr . tiryasyāh . seyam . padaprakr . tiriti. kim . kāran . am? padānyeva hi sam . hanyamānāni sam . hitā bhavati. tasmāt padānyeva hi prakr . tirvikārah . sam . hiteti. (Durga. 1.17 [28]).
  287. āha. kim . punaratra sādhīyah . padānām . prakr . titvam . sam . hitāyā vikāratvamuta vā vikāratvam . padānām . prakr . titvam . sam . hitāyā iti? ucyate sam . hitāyāh . prakr . titvam . jyāyah . . āha. kim . kāran . am? ucyate. mantro hyabhivyajyamānah . pūrvamr . s . ermantradr . śah . sam . hitayaivābhivyajyate na padaih . . (Durga. 1.17 [28]).
  288. Patañjali, right in the beginning of the Paspaśāhnika, mentions first the non-vedic expressions and then the vedic ones, which are sought to be instructed. kes . ām . śabdānām (anuśāsanam)? laukikānām . vaidikānām . ca | . . . vaidikāh . khalvapi | (Pas. Bhā. 2-3).
  289. tatra prativākyam . sam . ketagrahāsambhavād vākyānvākhyānasya laghūpāyena aśakyatvācca kalpanayā padāni pravibhajya pade prakr . tipratyayabhāgān pravibha- jya kalpitābhyām anvayavyatirekābhyām . tattadarthavibhāgam . śāstramātravis . ayam . parikalpayantismācāryāh . (V. L. [21] p. 7).
  290. Thieme suggested this to be the purpose of grammar, although his idea behind this suggestion was that grammar is a device having magical effect which 'works the truth' of the special nature of Sanskrit, sam . skr . tasya sam . skr . tatvam [26]. Houben differs [9], but Scharfe suggests that this direction deserves to be explored further [24] p.87. 27 raks . ohāgamalaghvasam . dehāh . prayojanam | (Pas. Vā. 2). laghvartham . cādhyeyam . vyākaran . am | (Pas. Bhā. 20) lāghavena śabdajñānamasya prayojanam | (Pradīpa on Pas. Bhā. 20). Table 1. Abbreviations
  291. A. As . t . ādhyāyī
  292. Uv. Uvat . a-bhās . ya on R . gveda-Prātiśākhya Cha. Śā. Chanda-Śāstra of Pi ṅgala Ni. Nirukta Pas. Paspaśāhnika Pas. Bā. Paspaśāhnika Bhās . ya Pas. Vā. Paspaśāhnika Vārtika Mu. Mun . d . akopanis . ad
  293. Mu. Bhā. Mun . d . akopanis . ad Śā ṅkara-Bhās . ya V. L. Vaiyākaran . a-siddhānta-parama-laghu-mañjūs .
  294. ā Vā. Prā. Vājasaneyi-Prātiśākhya Śu. Yaj. Śukla-Yajurveda References
  295. Bhise, U.R.: Nāradīyā Śiks . ā with the Commentary of Bhat . t . a Śobhākara. Bhan- darkar Oriental Research Institute, Poona (1986)
  296. von Böhtlingk, O.: Pān . ini's Grammatik. Olms, Hildesheim (1887)
  297. Bronkhorst, J.: Greater Magadha. Studies in the Culture of Early. Brill, Leiden (2007)
  298. Dhūpakara, A. Ś.: Śrīpi ṅgalanāga-viracitam . Chandah . śāstram. Parimal Publica- tions, Delhi (1985)
  299. Deshpande, M.M.: Semantics of Kārakas in Pān . ini: An Exploration of Philosoph- ical and Linguistical Issues. In: Matilal, B.K., Bilimoria, P. (eds.) Sanskrit and Related Studies: Contemporary Researches and Reflections, pp. 33-57. Sri Sat- guru Publications, Delhi (1990)
  300. Gladigow, B.: Sequenzierung von Riten und die Ordnung der Rituale. In: Stausberg, M. (ed.) Zorostrian Rituals in Context. Numen Book Series, Studies in the History of Religions, vol. CII, pp. 57-76. Brill, Leiden (1999)
  301. Joshi, S.D., Roodbergen, J.A.F.: On siddha, asiddha and sthānivat. Annals of the Bhandarkar Oriental Research Institute, Bhandarkar Oriental Research Institute, vol. LXVIII, pp. 541-549. Bhandarkar Oriental Research Institute, Poona (1987)
  302. Joshi, S.D., Roodbergen, J.A.F.: The As . t . ādhyāyī of Pān . ini. With Translation and Explanatory Notes, vol. II. Sahitya Akademi, New Delhi (1993)
  303. Houben, J.E.M.: 'Meaning statements' in Pān . ini's grammar: on the purpose and context of the As . t . ādhyāyī. Studien zur Indologie und Iranistik 22, 23-54 (1999- 2001)
  304. Houben, J.E.M.: Memetics of Vedic Ritual, Morphology of the Agnis . t . oma. In: Griffiths, A., Houben, J.E.M. (eds.) The Vedas: Texts, Language & Ritual, pp. 23-47. Forsten, Groningen (2004)
  305. Katre, S.M.: As . t . ādhyāyī of Pān . ini. Motilal Banarsidass, Delhi (1989)
  306. Lüders, H.: Die Vyâsa-C ¸ikshâ besonders in ihrem Verhältnis zum Taittirîya- Prâtiçâkhya. Göttingen (1894)
  307. Michaels, A.: 'Le rituel pour le rituel' oder wie sinnlos sind Rituale? In: Caduff, C., Pfaff-Czarnecka, J. (eds.) Rituale heute: Theorien -Kontroversen -Entwürfe, pp. 23-47. Reimer, Berlin (1999)
  308. Michaels, A.: The Grammar of Rituals. In: Michaels, A., Mishra, A. (eds.) Gram- mar and Morphology of Ritual, pp. 15-36. Harrassowitz Verlag, Wiesbaden (2010)
  309. Mishra, A.: Simulating the Pān . inian System of Sanskrit Grammar. In: Huet, G., Kulkarni, A., Scharf, P. (eds.) Sanskrit Computational Linguistics. LNCS (LNAI), vol. 5406, pp. 127-138. Springer, Heidelberg (2009)
  310. Mishra, A.: Modelling the Grammatical Circle of the Pān . inian System. In: Huet, G., Kulkarni, A., Scharf, P. (eds.) Sanskrit Computational Linguistics 2007/2008. LNCS (LNAI), vol. 5402, pp. 40-55. Springer, Heidelberg (2009)
  311. Mishra, A.: On the Possibilities of a Pān . inian Paradigm for a Rule-based Descrip- tion of Rituals. In: Michaels, A., Mishra, A. (eds.) Grammar and Morphology of Ritual, pp. 91-104. Harrassowitz Verlag, Wiesbaden (2010)
  312. Lakshman, S.: The Nighan . t . u and The Nirukta. Motilal Banarsidass, Delhi (1967)
  313. Oppitz, M.: Montageplan von Ritualen. In: Caduff, C., Pfaff-Czarnecka, J. (eds.) Rituale heute: Theorien -Kontroversen -Entwürfe, pp. 73-95. Reimer, Berlin (1999)
  314. Sarup, L.: The Nighan . t . u and the Nirukta. Motilal Banarasidass, Delhi (1967)
  315. Shastri, K.: Nāgeśabhat . t . a-kr . ta Vaiyākaran . a-siddhānta-parama-laghu-mañjūs . ā. Kurukshetra University Press, Kurukshetra (1975)
  316. Shastri, M.D.: The R . gveda-Prātiśākhya with the commentary of Uvat . a. Vaidika Svadhyaya Mandir, Varanasi (1959)
  317. Scharf, P.M.: Modeling Pān . inian Grammar. In: Huet, G., Kulkarni, A., Scharf, P. (eds.) Sanskrit Computational Linguistics 2007/2008. LNCS (LNAI), vol. 5402, pp. 95-126. Springer, Heidelberg (2009)
  318. Scharfe, H.: A new perspective on Pān . ini. In: Indologica Taurinensia, vol. XII, pp. 1-273 (2009)
  319. Staal, F.: Rules without meaning: ritual, mantras and the human sciences. Lang, New York (1989)
  320. Thieme, P.: Meaning and form of the 'grammar' of Pān . ini. Studien zur Indologie und Iranistik 8/9, 12-22 (1982)
  321. Varma, V.K. (ed.): Vājasaneyi-Prātiśākhyam. Chaukhamba Sanskrit Pratishthan, Delhi (1987)
  322. Varma, V.K.: R . gveda-Prātiśākhyam. Banaras Hindu University Sanskrit Series, Varanasi (1972)
  323. Wezler, A.: On the quadruple division of the Yogaśāstra, the caturvyūhatva of the Cikitsāśāstra and the 'four noble truths' of the Buddha. In: Indologica Taurinensia, vol. XII, pp. 289-337 (1984)

Related papers

Towards Computational Processing of Sanskrit
Gérard Huet

2003

We present in this paper recent progress in Sanskrit computational linguistics. We propose a solution to the tagging of verb phrases which correctly handles the non-associativity of external sandhi arising from the treatment of preverb ā. This involves a notion of phantom phoneme. We give exemples of execution of a computer tagger designed accordingly, which handles verb phrases as well as noun phrases.

View PDFchevron_right
Statistical Constituency parser for Sanskrit compounds
Amba Kulkarni

Sanskrit is very rich in compound formation. Typically a compound does not code the relation between its components explicitly. To understand the meaning of a compound, it is necessary to identify its components, identify the way the components group together, discover the relations between them and finally generate a paraphrase of the compound. In this paper, we discuss our efforts in building a constituency parser for Sanskrit compounds. The average performance of this parser is 85%.

View PDFchevron_right
Statistical Constituency parser for Sanskrit compounds Amba Kulkarni and Anil Kumar
Amba Kulkarni

Sanskrit is very rich in compound formation. Typically a compound does not code the relation between its components explicitly. To understand the meaning of a compound, it is necessary to identify its components, identify the way the components group together, discover the relations between them and finally generate a paraphrase of the compound. In this paper, we discuss our efforts in building a constituency parser for Sanskrit compounds. The average performance of this parser is 85%.

View PDFchevron_right
Proceedings of First International Symposium on Sanskrit Computational Linguistics
Amba Kulkarni

2008

This volume contains the proceedings of the First International Symposium on Sanskrit Computational Linguistics, held at the Paris-Rocquencourt Research Center of INRIA form the 29th to the 31st of October 2007. The grammatical tradition of Sanskrit and Computational Linguistics–both have a lot to offer to each other.

View PDFchevron_right
Generation of Sanskrit Compounds
Amba Kulkarni

Sanskrit is rich in compound formation. Pān . ini's grammar has more than 400 rules that deal with the semantic conditions governing the compound formation and the grammatical process involved in the compound formation. In this paper we rewrite the grammar as a combination of phrase structure rules and the regular grammar and list various semantic features as constraints governing the formation of compounds in Sanskrit.

View PDFchevron_right

Related topics

  • Optimality Theory

    • Cited by

    Hybridization Based Machine Translations for Low-Resource Language with Language Divergence
    Poonam Bansal

    ACM Transactions on Asian and Low-Resource Language Information Processing

    A hybridised form of direct and rule-based language processing is used in this paper to present a Machine translation system from Sanskrit to Hindi. The divergence between Sanskrit and Hindi is also discussed in this paper, along with a proposition for how to handle it. Sanskrit-Hindi bilingual dictionaries, Grammatical Sanskrit corpus and a Sanskrit analyses rule base, have all been used in the projected system. The projected system&#39;s ability to access data from various data vocabularies and rule bases utilised in the system expansion has been improved by the usage of Elasticsearch technique. Additionally, a novel technique that builds a parse tree from the parsing table is presented in this paper. The system processes the input Sanskrit sentence using the parsing approach and the Context Free Grammar in normal form for Sanskrit language processing. No standard Sanskrit-Hindi Grammatical corpora available for Machine Translation which is designed and developed in the proposed w...

    View PDFchevron_right
    Later Nyāya Logic: Computational Aspects
    Amba Kulkarni

    2018

    In this article we describe the computational aspects of the Technical Language of Navya-Nyāya. Navya-Nyaya is an offshoot of the early Nyāya philosophy. It deviates from the Nyāya philosophy in three major ways. First, instead of prameyas the discussions are centered around the pramāṇas. Second, the Navya-Nyāya has adapted the Vaiseṣika ontology. And finally it has introduced a few concepts expressed through an unambiguous technical terminology that brings in a clarity in the communication removing the inherent ambiguities of a natural language. In this article the syntax of expressions involving this technical terminology is described, followed by a scheme based on Conceptual Graphs of Sowa for their graphical rendering. Finally a computational algorithm is described that renders the graphs corresponding to the Navya-Nyāya expressions semi-automatically.

    View PDFchevron_right
    580 California St., Suite 400
    San Francisco, CA, 94104
    © 2025 Academia. All rights reserved