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Table 5.49: Relations between verbs and their nominalisations — part 1

Table 5 49: Relations between verbs and their nominalisations — part 1

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Table 2.1: Complements and adjuncts in different studies ie a DA Ne A Ne hE lve The statements described in the two sections above show that the distinction be- tween obligatory and optional complements, as well as the one between comple- ments and adjuncts is analysed both, in linguistic and NLP studies, where it finds different terminological description. In table 2.1 we summarise some of the above mentioned descriptions, according to the studies they are used in.
Table 2.1: Complements and adjuncts in different studies ie a DA Ne A Ne hE lve The statements described in the two sections above show that the distinction be- tween obligatory and optional complements, as well as the one between comple- ments and adjuncts is analysed both, in linguistic and NLP studies, where it finds different terminological description. In table 2.1 we summarise some of the above mentioned descriptions, according to the studies they are used in.
The principal grammatical functions in FrameNet , e.g. External Argument, Ob- ject, and Dependent, are described in (Filllmore 2007). External corresponds to the subject. The term subject is not used in the theory of Frame Semantics because in most cases the relevant constituent in its own location is not a subject. The grammat- ical function External is used for the subject function of both, finite and non-finite verbs (cf. (2.6a) and (2.6b) respectively), as well as for dependents of governing or frame-bearing nouns, cf. (2.6c). Thus, in (2.6b) the general, which is the Object of persuade, is the External Argument of to release and in the sentence (2.6c), physician is the External argument of the verb perform but has the Genitive relation to the noun decision. In some cases, nominal predicates have their own External, e.g. if used with support verbs within a multiword expression as in (2.6d) or when the frame-bearing noun is governed by a control noun, e.g. in (2.6e). LFG!”, grammatical functions provide a mapping between the syntactic structures and the predicate-argument structure. The grammar of each language characterises, which syntactic constituent can be mapped onto the argument of a predicate. The characterisations differ from language to language, but the functions remain the same — all languages have subjects, objects, oblique objects and complements of various types, which can be mapped onto the arguments of verbal or other predicates (see (Bresnan 1982a)). Grammatical functions in LFG are classified into two groups - subcategorisable and non-subcategorisable (for adjuncts). Subcategorisable func- tions are subdivided into semantically unrestricted functions, which include SUBJ (subject), OBJ and OBJ2 (objects), and semantically restricted ones, which contain OBLg (prepositional object), COMP and XCOMP (complex objects expressed by com- plement clauses).
The principal grammatical functions in FrameNet , e.g. External Argument, Ob- ject, and Dependent, are described in (Filllmore 2007). External corresponds to the subject. The term subject is not used in the theory of Frame Semantics because in most cases the relevant constituent in its own location is not a subject. The grammat- ical function External is used for the subject function of both, finite and non-finite verbs (cf. (2.6a) and (2.6b) respectively), as well as for dependents of governing or frame-bearing nouns, cf. (2.6c). Thus, in (2.6b) the general, which is the Object of persuade, is the External Argument of to release and in the sentence (2.6c), physician is the External argument of the verb perform but has the Genitive relation to the noun decision. In some cases, nominal predicates have their own External, e.g. if used with support verbs within a multiword expression as in (2.6d) or when the frame-bearing noun is governed by a control noun, e.g. in (2.6e). LFG!”, grammatical functions provide a mapping between the syntactic structures and the predicate-argument structure. The grammar of each language characterises, which syntactic constituent can be mapped onto the argument of a predicate. The characterisations differ from language to language, but the functions remain the same — all languages have subjects, objects, oblique objects and complements of various types, which can be mapped onto the arguments of verbal or other predicates (see (Bresnan 1982a)). Grammatical functions in LFG are classified into two groups - subcategorisable and non-subcategorisable (for adjuncts). Subcategorisable func- tions are subdivided into semantically unrestricted functions, which include SUBJ (subject), OBJ and OBJ2 (objects), and semantically restricted ones, which contain OBLg (prepositional object), COMP and XCOMP (complex objects expressed by com- plement clauses).
Table 2.5: Forms of sentential complements in German.
Table 2.5: Forms of sentential complements in German.
[Types of valency information related to this study] We summarise the main types of subcategorisation information analysed in the described studies above (e.g. valency patterns (or frames), grammatical functions (GF), case, syntactic categories (SCs), semantic roles (SemR) and selectional restrictions (SelR) in table 2.12.
[Types of valency information related to this study] We summarise the main types of subcategorisation information analysed in the described studies above (e.g. valency patterns (or frames), grammatical functions (GF), case, syntactic categories (SCs), semantic roles (SemR) and selectional restrictions (SelR) in table 2.12.
Table 3.1: Predicate types and studies on them Subcategorisation features of different predicates have been an important researc object in linguistics and lexicography. (Agel 2000) notes that there are more thar 3000 publications dealing with valency theoretical problems. We do not aim at re viewing all the issues analysed in those studies. We only want to discuss the aspect: relevant for the present research, cf. section 2.3 above. Concentrating on differen types of predicates in our study, we refer to a number of studies on valency of these predicates, including linguistic theoretical work, subcategorisation dictionaries, a: well as works on acquisition tools. Table 3.1 contains a list of works we will study ir this chapter. Most authors concentrate on the analysis of verbal predicates. There are studies, which also describe subcategorisation properties of nominal predicates, for exam- ple, (Teubert 1979) or (Schierholz 2005), and multiword predicates, for instance, (Krenn/Erbach 1994) or (Fellbaum et al 2006), which are mostly discussed in con- nection with verbal predicates, i.e. when the properties of verbs are compared with the properties of their derivatives.
Table 3.1: Predicate types and studies on them Subcategorisation features of different predicates have been an important researc object in linguistics and lexicography. (Agel 2000) notes that there are more thar 3000 publications dealing with valency theoretical problems. We do not aim at re viewing all the issues analysed in those studies. We only want to discuss the aspect: relevant for the present research, cf. section 2.3 above. Concentrating on differen types of predicates in our study, we refer to a number of studies on valency of these predicates, including linguistic theoretical work, subcategorisation dictionaries, a: well as works on acquisition tools. Table 3.1 contains a list of works we will study ir this chapter. Most authors concentrate on the analysis of verbal predicates. There are studies, which also describe subcategorisation properties of nominal predicates, for exam- ple, (Teubert 1979) or (Schierholz 2005), and multiword predicates, for instance, (Krenn/Erbach 1994) or (Fellbaum et al 2006), which are mostly discussed in con- nection with verbal predicates, i.e. when the properties of verbs are compared with the properties of their derivatives.
Figure 3.2: F-structure for the sentence John believes that Howard loves Sue.
Figure 3.2: F-structure for the sentence John believes that Howard loves Sue.
In table 3.5, we summarise the features of the above described formal grammars and NLP-based dictionaries according to the types of subcategorisation information, rele- vant for this study: valency patterns (VP), grammatical functions (GF), syntactic cat- egories (SC), selectional restrictions (SelR) and semantic roles (SemR), as described for lexicographic work in table 3.2 in section 3.1.1.3 above. 2 Subcategorisation of Nouns
In table 3.5, we summarise the features of the above described formal grammars and NLP-based dictionaries according to the types of subcategorisation information, rele- vant for this study: valency patterns (VP), grammatical functions (GF), syntactic cat- egories (SC), selectional restrictions (SelR) and semantic roles (SemR), as described for lexicographic work in table 3.2 in section 3.1.1.3 above. 2 Subcategorisation of Nouns
Figure 3.9: Entry from (Sommerfeldt/Schreiber 1983) for the noun Beurteilung
Figure 3.9: Entry from (Sommerfeldt/Schreiber 1983) for the noun Beurteilung
Table 3.6: Nominal valency in lingistic and lexicographic work In table 3.6, we summarise the above described linguistic and lexicographic work according to the types of subcategorisation information relevant for the analysis of nominal predicates: valency patterns (VP), syntactic categories (SC) and selectional restrictions or semantic roles (SR).
Table 3.6: Nominal valency in lingistic and lexicographic work In table 3.6, we summarise the above described linguistic and lexicographic work according to the types of subcategorisation information relevant for the analysis of nominal predicates: valency patterns (VP), syntactic categories (SC) and selectional restrictions or semantic roles (SR).
Figure 3.12: F-structure for the phrase die Ermittlung, ob die Antwort stimmt. ?0All the three F-structures were generated with the XLE-Web, web-based tool for parsing with LFG http://decentius.aksis.uib.no/logon/xle.xml Valency patterns In the HPSG framework subcategorisation patterns for nouns are also expressed in the feature SUBCAT, cf. the description of SUBCAT for verbal va- lency in section 3.1.2.1 above. In HPSG subcategorisation features of nouns are applied to build a nominal phrase whose head is a noun: NP — DPN’. To build a nominal phrase a noun or a nominal complex (saturated) needs a determiner, cf. (Pollard/Sag 1994), thus, the SUBCAT of a nominal phrase containing a noun con- ains the structure for a determiner phrase DP, as shown in figure 3.11. The saturated V’ can be either a single noun or a combination of a noun with further elements, e.g. phrases in genitive, such as Entscheidung der Frau, or prepositional phrases, such as Entschediung gegen mich, etc.
Figure 3.12: F-structure for the phrase die Ermittlung, ob die Antwort stimmt. ?0All the three F-structures were generated with the XLE-Web, web-based tool for parsing with LFG http://decentius.aksis.uib.no/logon/xle.xml Valency patterns In the HPSG framework subcategorisation patterns for nouns are also expressed in the feature SUBCAT, cf. the description of SUBCAT for verbal va- lency in section 3.1.2.1 above. In HPSG subcategorisation features of nouns are applied to build a nominal phrase whose head is a noun: NP — DPN’. To build a nominal phrase a noun or a nominal complex (saturated) needs a determiner, cf. (Pollard/Sag 1994), thus, the SUBCAT of a nominal phrase containing a noun con- ains the structure for a determiner phrase DP, as shown in figure 3.11. The saturated V’ can be either a single noun or a combination of a noun with further elements, e.g. phrases in genitive, such as Entscheidung der Frau, or prepositional phrases, such as Entschediung gegen mich, etc.
Figure 3.13: F-structure for the phrase sein Stolz auf dieses Projekt
Figure 3.13: F-structure for the phrase sein Stolz auf dieses Projekt
Table 3.9: Compliment patterns for the noun announcement in NOMLEX Nominal subcategorisation patterns in STO are encoded with the help of letters and digits, e.g. Dn2GPn-med, where ’Dn’ means the start of the lexical description for a noun, ’2’ - the number of arguments this noun can has. The letter ’G’ stands for genitive, Pn’ — for a preposition complement, which is governed by a nominal phrase ’n’, med’ — for the preposition med ("with/by"). The authors sometimes en- code modifiers along with the argument, if they occur frequently and are part of the ‘core meaning’ of the noun.
Table 3.9: Compliment patterns for the noun announcement in NOMLEX Nominal subcategorisation patterns in STO are encoded with the help of letters and digits, e.g. Dn2GPn-med, where ’Dn’ means the start of the lexical description for a noun, ’2’ - the number of arguments this noun can has. The letter ’G’ stands for genitive, Pn’ — for a preposition complement, which is governed by a nominal phrase ’n’, med’ — for the preposition med ("with/by"). The authors sometimes en- code modifiers along with the argument, if they occur frequently and are part of the ‘core meaning’ of the noun.
In sections 3.2.2.1 and 3.2.2.2 above we analyse different NLP approaches on the description of nominal subcategorisation. We summarise these approaches in ta- ble 3.10, indicating which types of subcategorisation information are present in the above described studies (the relevant types of information are definbed in section 3.2.1.3) above. Table 3.10: Nominal valency in NLP work 3.3 Subcategorisation of Multiword Expressions
In sections 3.2.2.1 and 3.2.2.2 above we analyse different NLP approaches on the description of nominal subcategorisation. We summarise these approaches in ta- ble 3.10, indicating which types of subcategorisation information are present in the above described studies (the relevant types of information are definbed in section 3.2.1.3) above. Table 3.10: Nominal valency in NLP work 3.3 Subcategorisation of Multiword Expressions
The description of valency of multiword predicates id summarised in table 3.14. Table 3.14: Valency of multiwords in NLP dictionaries
The description of valency of multiword predicates id summarised in table 3.14. Table 3.14: Valency of multiwords in NLP dictionaries
Figure 3.21: F-structure for the sentence Wir nehmen Einfluss auf seine Entwicklung.
Figure 3.21: F-structure for the sentence Wir nehmen Einfluss auf seine Entwicklung.
I OI (Johnston/Busa 1999) analysing English and Italian compounds, specify their yredicate-argument structures within the qualia structures of the head nouns. Qualia tructures provide the ’glue’, which links the semantic contributions of modifying 1onheads and those of heads. The authors apply the representational framework yf the Generative Lexicon (GL) introduced by Pustejovsky’, simplifying a GL entry o four levels of representations: type structure, argument structure, event structure ind qualia structure, as shown in figure 3.22. The latter expresses four aspects of the neaning of the lexical item: FORMAL,CONSTITUTIVE, TELIC, and AGENTIVE. The nterpretation of the compound form hunting rifle can sound as follows ’a rifle which s used in its typical capacity (i.e. firing) for the purpose of performing the activity of 1unting’. The assignment of a complex structure to an individual quale is coherent vith the general interpretation of qualia structure. 33For example, in (Pustejovky 1995).
I OI (Johnston/Busa 1999) analysing English and Italian compounds, specify their yredicate-argument structures within the qualia structures of the head nouns. Qualia tructures provide the ’glue’, which links the semantic contributions of modifying 1onheads and those of heads. The authors apply the representational framework yf the Generative Lexicon (GL) introduced by Pustejovsky’, simplifying a GL entry o four levels of representations: type structure, argument structure, event structure ind qualia structure, as shown in figure 3.22. The latter expresses four aspects of the neaning of the lexical item: FORMAL,CONSTITUTIVE, TELIC, and AGENTIVE. The nterpretation of the compound form hunting rifle can sound as follows ’a rifle which s used in its typical capacity (i.e. firing) for the purpose of performing the activity of 1unting’. The assignment of a complex structure to an individual quale is coherent vith the general interpretation of qualia structure. 33For example, in (Pustejovky 1995).
Figure 3.24: NOMLEX entry for the noun experiment The PARC text processing system, cf. (Gurevich et al. 2007), by contrast does not have a separate level for nominal argument structures. The system is based on se- mantic description of verbal arguments. Deverbal nouns and their arguments are converted into verb-like event structures. The authors make an attempt to canonise deverbal nouns making them look like verbs for the purposes of knowledge repre- sentation. This is important for reasoning systems that rely on semantic information from the input. For example, such a system should be able to answer the question in (3.26a) based on the sentence in (3.26b). The entries of NOMLEX indicate, which verb underlies the given nominalisations, and which complements of the nominalisation are inherited from its base verb?®, cf. table 3.9 in section 3.2.2.2 above. In figure 3.24, we give an entry that shows arguments of the nominalisation experiment whose subject can be a possessive, or a noun-noun modifier. Further arguments can be raelised as prepositional phrases only as the underlying verb to experiment is instransitive.*? The subject of the verb (VERB- SUBJ) may appear as noun-noun modifier (N-N-MOD), like in laboratory experiment or as possessive determiner, like in my experiment. The object can have the form of a prepositional phrase (NOM-PP), e.g. headed by the prepositions on or with. The complementation types are prefixed with NOM (NOM-INTRANS, NOM-PP)
Figure 3.24: NOMLEX entry for the noun experiment The PARC text processing system, cf. (Gurevich et al. 2007), by contrast does not have a separate level for nominal argument structures. The system is based on se- mantic description of verbal arguments. Deverbal nouns and their arguments are converted into verb-like event structures. The authors make an attempt to canonise deverbal nouns making them look like verbs for the purposes of knowledge repre- sentation. This is important for reasoning systems that rely on semantic information from the input. For example, such a system should be able to answer the question in (3.26a) based on the sentence in (3.26b). The entries of NOMLEX indicate, which verb underlies the given nominalisations, and which complements of the nominalisation are inherited from its base verb?®, cf. table 3.9 in section 3.2.2.2 above. In figure 3.24, we give an entry that shows arguments of the nominalisation experiment whose subject can be a possessive, or a noun-noun modifier. Further arguments can be raelised as prepositional phrases only as the underlying verb to experiment is instransitive.*? The subject of the verb (VERB- SUBJ) may appear as noun-noun modifier (N-N-MOD), like in laboratory experiment or as possessive determiner, like in my experiment. The object can have the form of a prepositional phrase (NOM-PP), e.g. headed by the prepositions on or with. The complementation types are prefixed with NOM (NOM-INTRANS, NOM-PP)
Table 4.3: Verb classification according to different aspects In table 4.3 we summarise a number of studies on verb classification sorting them according to the used classification criteria. Aspectual features are inheritent tem- poral properties of verbs, semantic aspects are based on verb meaning and syntactic ones are based on verb behaviour or verbal subcategorisation properties.
Table 4.3: Verb classification according to different aspects In table 4.3 we summarise a number of studies on verb classification sorting them according to the used classification criteria. Aspectual features are inheritent tem- poral properties of verbs, semantic aspects are based on verb meaning and syntactic ones are based on verb behaviour or verbal subcategorisation properties.
Table 4.4: Complementation classes according to (Klotz 2007) The author semantically classifies verbs of complementation classes 2 and 5 (those that can take only that-clauses vs. those allowing for both that-clauses and infinitive complements). The author analyses 112 verbs, which are classified into 9 semantic groups: communication verbs, opinion verbs, fact finding verbs, fact demon- strating verbs, fact manipulating verbs, fact establishing verbs, emotion verbs, imagination verbs and unclassified, such as respect, vote and wonder. The tested
Table 4.4: Complementation classes according to (Klotz 2007) The author semantically classifies verbs of complementation classes 2 and 5 (those that can take only that-clauses vs. those allowing for both that-clauses and infinitive complements). The author analyses 112 verbs, which are classified into 9 semantic groups: communication verbs, opinion verbs, fact finding verbs, fact demon- strating verbs, fact manipulating verbs, fact establishing verbs, emotion verbs, imagination verbs and unclassified, such as respect, vote and wonder. The tested
Compound cominals can also be subclassified into further groups according to mor- phological or derivational criteria or according to the relations between their head and non-head constituents. In this section, we analyse related work on classifica- tion of compound nominals. In table 4.8, we list classes of compound predicates according to the classification criteria applied in approaches. Table 4.8: Classes of compound nominal predicates according to different criteria
Compound cominals can also be subclassified into further groups according to mor- phological or derivational criteria or according to the relations between their head and non-head constituents. In this section, we analyse related work on classifica- tion of compound nominals. In table 4.8, we list classes of compound predicates according to the classification criteria applied in approaches. Table 4.8: Classes of compound nominal predicates according to different criteria
}.2.4.1 Criteria of compositionality or lexicalisation grade (Breidt 1993) classifies multiwords into verbal phrasemes (idioms), such as to take a fancy, support verb constructions, e.g. to take into consideration and colloca- tions in the narrow sense (a combination of the support verb with a concrete or non-predicative noun), like to take a seat?°. The author claims that the difference between these types is very gradual and it is difficult to define the criteria for their distinction. .2.4 Multiwords and their Classification
}.2.4.1 Criteria of compositionality or lexicalisation grade (Breidt 1993) classifies multiwords into verbal phrasemes (idioms), such as to take a fancy, support verb constructions, e.g. to take into consideration and colloca- tions in the narrow sense (a combination of the support verb with a concrete or non-predicative noun), like to take a seat?°. The author claims that the difference between these types is very gradual and it is difficult to define the criteria for their distinction. .2.4 Multiwords and their Classification
Table 4.12: Classes of support verb constructions based on their lexicalisation (Winhart 2002), who concentrates on SVCs in German, distinguishes between lexicalised and non-lexicalised support verb constructions, following the classification given e.g. by Kuhn?!, cf. table 4.12. 4.2.4.2 Structural and syntactic criteria
Table 4.12: Classes of support verb constructions based on their lexicalisation (Winhart 2002), who concentrates on SVCs in German, distinguishes between lexicalised and non-lexicalised support verb constructions, following the classification given e.g. by Kuhn?!, cf. table 4.12. 4.2.4.2 Structural and syntactic criteria
Table 4.13: Classes of multiwords in FrameNet D the presence of a prepositional phrase in the SVC;
Table 4.13: Classes of multiwords in FrameNet D the presence of a prepositional phrase in the SVC;
In the M3 multiwords, neither their nominal nor their verbal component subcate- gorise for a sentential complement, whereas the multiword itself does. We also group in this class multiwords whose noun takes a subclause, but in a massively different subcategorisation frame: Beweis (“proof”) takes a ftir-PP or a sentential complement with a(n optional) Korrelat (daftir), whereas unter Beweis stellen (“to provide evi- dence for”), which also has a sentence complement, can never take the correlate nor a fiir-PP. These cases are also semantically transparent, i.e. do not qualify for the status of idioms. 7
In the M3 multiwords, neither their nominal nor their verbal component subcate- gorise for a sentential complement, whereas the multiword itself does. We also group in this class multiwords whose noun takes a subclause, but in a massively different subcategorisation frame: Beweis (“proof”) takes a ftir-PP or a sentential complement with a(n optional) Korrelat (daftir), whereas unter Beweis stellen (“to provide evi- dence for”), which also has a sentence complement, can never take the correlate nor a fiir-PP. These cases are also semantically transparent, i.e. do not qualify for the status of idioms. 7
Table 4.15: Subcategorisation “inheritance” types the semi-automatic classification described in (Lapshinova 2009). Our classification is based on the description of “inheritance” and “non-inheritance” relations between deverbal nouns and their base verbs presented in section 3.4.2 above. Some nom- inalisations inherit verbal predicates (“inheritance” cases), some of themlose a part of the verbal subcategorisation features (“inheritance” reduction) and some of them gain subcategorisation properties, which are not specific for their verbs (“inheritance” extension), cf. section 3.4.2 above. Therefore, we distinguish between three classes of subcategorisation relations: R1, R2 and R3, cf. table 4.15.
Table 4.15: Subcategorisation “inheritance” types the semi-automatic classification described in (Lapshinova 2009). Our classification is based on the description of “inheritance” and “non-inheritance” relations between deverbal nouns and their base verbs presented in section 3.4.2 above. Some nom- inalisations inherit verbal predicates (“inheritance” cases), some of themlose a part of the verbal subcategorisation features (“inheritance” reduction) and some of them gain subcategorisation properties, which are not specific for their verbs (“inheritance” extension), cf. section 3.4.2 above. Therefore, we distinguish between three classes of subcategorisation relations: R1, R2 and R3, cf. table 4.15.
Table 5.1: Corpora used in the study
Table 5.1: Corpora used in the study
Table 5.7: Subclauses after verbal predicates in VL
Table 5.7: Subclauses after verbal predicates in VL
Table 5.9: Subclause after verbal predicates in passive Structure and features In a passive sentence verbal predicates have a complex form and can occupy both the LSK and the RSK. The first part of the verbal predicate, which is either an auxiliary or a modal verb, fills the LSK. The RSK contains either the full verb in form of a participle or the combination of it with another auxiliary, e.g. werden (“to get/be”) or sein (“to be”), which constitute the second part of the verbal complex.
Table 5.9: Subclause after verbal predicates in passive Structure and features In a passive sentence verbal predicates have a complex form and can occupy both the LSK and the RSK. The first part of the verbal predicate, which is either an auxiliary or a modal verb, fills the LSK. The RSK contains either the full verb in form of a participle or the combination of it with another auxiliary, e.g. werden (“to get/be”) or sein (“to be”), which constitute the second part of the verbal complex.
In the v1 sentences, the Vorfeld is empty and the first position in the sentence is occupied by the finite verb, which can be a full, a modal or an auxiliary verb (verb1 in table 5.11). If the sentences starts with a modal or an auxiliary verb, the full verb infinitive or participle precedes the subcategorised clause in the Nachfeld. In the v2 sentences, the Vorfeld is not empty and can be occupied by various constituents - subject, objects, place or time descriptions, etc. The finite verb occupies the sec- ond position (verb1). If the finite verb is modal or auxiliar, the position before the Nachfeld is occupied by the full verb infinitive or participle, cf. table 5.11. These sentence models are more common in corpora. However, they deliver a eR Renee anae Meese: be: NA ee er Net Det Seen, Pat Eee eet N: Meteo cheese ee) See RAR See eg Se: SE ee) M8 tS These sentence models are more common in corpora. However, they deliver a large number of non-relevant cases, thus, reducing the accuracy of our extraction procedures. Most problems are caused by non-verbal constituents, which occupy various positions in v1 or v2 and can be predicates themselves. The position of the valency bearer in these cases is not as predictable as in the VL and passive sentences. For instance, the MF in 1a can contain a subclause-taking noun, e.g. Grund (“rea- son”). In this case the noun Grund and not the verb erkldren subcategorises for the w-clause in the NF, cf. 1a in table 5.12.
In the v1 sentences, the Vorfeld is empty and the first position in the sentence is occupied by the finite verb, which can be a full, a modal or an auxiliary verb (verb1 in table 5.11). If the sentences starts with a modal or an auxiliary verb, the full verb infinitive or participle precedes the subcategorised clause in the Nachfeld. In the v2 sentences, the Vorfeld is not empty and can be occupied by various constituents - subject, objects, place or time descriptions, etc. The finite verb occupies the sec- ond position (verb1). If the finite verb is modal or auxiliar, the position before the Nachfeld is occupied by the full verb infinitive or participle, cf. table 5.11. These sentence models are more common in corpora. However, they deliver a eR Renee anae Meese: be: NA ee er Net Det Seen, Pat Eee eet N: Meteo cheese ee) See RAR See eg Se: SE ee) M8 tS These sentence models are more common in corpora. However, they deliver a large number of non-relevant cases, thus, reducing the accuracy of our extraction procedures. Most problems are caused by non-verbal constituents, which occupy various positions in v1 or v2 and can be predicates themselves. The position of the valency bearer in these cases is not as predictable as in the VL and passive sentences. For instance, the MF in 1a can contain a subclause-taking noun, e.g. Grund (“rea- son”). In this case the noun Grund and not the verb erkldren subcategorises for the w-clause in the NF, cf. 1a in table 5.12.
Table 5.11: Subclause after verbal predicates in V1 and V2
Table 5.11: Subclause after verbal predicates in V1 and V2
Table 5.12: Noise in extraction of verbal predicates in V1 and V2
Table 5.12: Noise in extraction of verbal predicates in V1 and V2
Table 5.13: False negatives after exclusion of nouns in front of a subclause
Table 5.13: False negatives after exclusion of nouns in front of a subclause
Table 5.15: A noun in the VF subcategorising for a sentential complement.
Table 5.15: A noun in the VF subcategorising for a sentential complement.
Table 5.14: Forms of a full NP in the Vorfeld
Table 5.14: Forms of a full NP in the Vorfeld
Multiword expressions are extracted from the same contexts used for verbal predi- cates. Thus, we use the VL and passive constructions to extract multiwords, which contain a prepositional phrase, a noun and a support verb, cf. section 3.3 for the def- inition of the multiwords under analysis. In the VL sentence, the support verb occu- pies LSK, whereas the preposition and the noun of the multiword tend to immediately precede the verb. The subclause following the verb after a comma is subcategorised by the whole multiword expression, cf. table 5.16.
Multiword expressions are extracted from the same contexts used for verbal predi- cates. Thus, we use the VL and passive constructions to extract multiwords, which contain a prepositional phrase, a noun and a support verb, cf. section 3.3 for the def- inition of the multiwords under analysis. In the VL sentence, the support verb occu- pies LSK, whereas the preposition and the noun of the multiword tend to immediately precede the verb. The subclause following the verb after a comma is subcategorised by the whole multiword expression, cf. table 5.16.
Table 5.18: Forms of the PP in the searched multiword expressions
Table 5.18: Forms of the PP in the searched multiword expressions
Table 5.19: Query for subclause-taking predicates in passive
Table 5.19: Query for subclause-taking predicates in passive
Table 5.21: Specification to extract nominal predicates in the VF
Table 5.21: Specification to extract nominal predicates in the VF
OF The full NOMIMNa pnrdase, dS SNOWYT) 11) CaDle 9.14 If) SeCCtION 9.4.4 dDOVE. As stated in table 5.14, the NP in the VF can contain an optional determiner (an article, indefinite, demonstrative or a possessive pronoun specified in lines 4b, 4e and 4j). It can also contain optional adjectives, adverbs and other elements, the number of which we limit to 4 (lines 4c, 4f and 4k) to avoid the noise. The constraint for the nominal predicate to extract is contained in lines 4d, 4g and 41). Option 2 includes constraints for a NP in genetive (lines 4h and 4i), whereas option 3 includes constraints for a PP (4m and 4n) which can occur within the searched NP in the VF. The above described general queries are outlined in the scheme, illustrated in figure 5.6 below. This scheme represents a segment of the whole architecture for the 1 4 om 7 re 4
OF The full NOMIMNa pnrdase, dS SNOWYT) 11) CaDle 9.14 If) SeCCtION 9.4.4 dDOVE. As stated in table 5.14, the NP in the VF can contain an optional determiner (an article, indefinite, demonstrative or a possessive pronoun specified in lines 4b, 4e and 4j). It can also contain optional adjectives, adverbs and other elements, the number of which we limit to 4 (lines 4c, 4f and 4k) to avoid the noise. The constraint for the nominal predicate to extract is contained in lines 4d, 4g and 41). Option 2 includes constraints for a NP in genetive (lines 4h and 4i), whereas option 3 includes constraints for a PP (4m and 4n) which can occur within the searched NP in the VF. The above described general queries are outlined in the scheme, illustrated in figure 5.6 below. This scheme represents a segment of the whole architecture for the 1 4 om 7 re 4
Adverbial relative clauses introduced by the pronoun wo The preliminary analy- sis of the extracted predicates, which take subclauses introduced by the interrogative pronoun wo (“where”), shows that about 60% of these cases prove to be adverbial relative clauses, thus, false positives in our extraction results. As subclauses intro- duced by wo comprise about 22,6% of all w-clauses extracted in the VL context, they cause roughly 13% of the inaccuracy in the extraction results’.
Adverbial relative clauses introduced by the pronoun wo The preliminary analy- sis of the extracted predicates, which take subclauses introduced by the interrogative pronoun wo (“where”), shows that about 60% of these cases prove to be adverbial relative clauses, thus, false positives in our extraction results. As subclauses intro- duced by wo comprise about 22,6% of all w-clauses extracted in the VL context, they cause roughly 13% of the inaccuracy in the extraction results’.
Table 5.24: Constraints to exclude noisy cases with correlative expressions (5.12) Weil er sich seit Jahrhunderten dort wohl ftihIt, wo sie siedeln. (“Because for hundred of years he feels good there where they live”). l2We analysed 912 predicates extracted along with w-clauses from ’taz’. Sublauses introduced by wo comprise 22,6% (206) of all w-clauses. More than half of these cases (ca. 60%) prove to be false positives.
Table 5.24: Constraints to exclude noisy cases with correlative expressions (5.12) Weil er sich seit Jahrhunderten dort wohl ftihIt, wo sie siedeln. (“Because for hundred of years he feels good there where they live”). l2We analysed 912 predicates extracted along with w-clauses from ’taz’. Sublauses introduced by wo comprise 22,6% (206) of all w-clauses. More than half of these cases (ca. 60%) prove to be false positives.
To filter out such cases from the obtained data, we use the filtering query illus- trated in table 5.26. The query contains constraints for the pronoun wo that occurs after the NP in VF. Table 5.25: Lexical constraints to exclude “place”-nominals in the main clause
To filter out such cases from the obtained data, we use the filtering query illus- trated in table 5.26. The query contains constraints for the pronoun wo that occurs after the NP in VF. Table 5.25: Lexical constraints to exclude “place”-nominals in the main clause
Figure 5.7: Filtering in the extraction and classification architecture Summary The procedures to filtering out noise-causing cases is integrated into the cascaded architecture. Some of them, e.g. the procedures to eliminate the occurrence of headless relatives and adverbial relative clauses are integrated into the general queries. Others are applied on the subcorpora obtained with general queries. In this case, we filter out the irrelevant cases by removing them from the obtained data. The segment of the extraction architecture which specifies filtering procedures is akaxkicws te Gavean EF 7
Figure 5.7: Filtering in the extraction and classification architecture Summary The procedures to filtering out noise-causing cases is integrated into the cascaded architecture. Some of them, e.g. the procedures to eliminate the occurrence of headless relatives and adverbial relative clauses are integrated into the general queries. Others are applied on the subcorpora obtained with general queries. In this case, we filter out the irrelevant cases by removing them from the obtained data. The segment of the extraction architecture which specifies filtering procedures is akaxkicws te Gavean EF 7
Table 5.29: Lexical specification of the query to extract verbs in passive
Table 5.29: Lexical specification of the query to extract verbs in passive
Table 5.30: Query to extract multiwords in VL
Table 5.30: Query to extract multiwords in VL
Figure 5.8: List of nominal predicates after the analysis with SMOR Having the information about morphological features of nominal predicates, we can automatically sort out those, which contain the <V>-feature, thus, being derived from verbs. The list of nouns sorted form the list in figure 5.8 contains the nouns Bedenken, Entdeckung, Erfahrung, Frage, Verantwortung and Vorstellung. These nominalisations can be sorted further according to their morphological type: e.g - en or -ung-nominalisations. For example, if we extract -ung-nominalisations, we search for the nouns containing <V>ung<SUFF> only. The nominalisations with the suffix -ung are especially interesting in the analysis of the subcategorisation relations between verbs and their nominalisations. We can also search for this nominalisation type in corpora using a lexically specified CQP-query. oe oy , a i i i i. ee ae
Figure 5.8: List of nominal predicates after the analysis with SMOR Having the information about morphological features of nominal predicates, we can automatically sort out those, which contain the <V>-feature, thus, being derived from verbs. The list of nouns sorted form the list in figure 5.8 contains the nouns Bedenken, Entdeckung, Erfahrung, Frage, Verantwortung and Vorstellung. These nominalisations can be sorted further according to their morphological type: e.g - en or -ung-nominalisations. For example, if we extract -ung-nominalisations, we search for the nouns containing <V>ung<SUFF> only. The nominalisations with the suffix -ung are especially interesting in the analysis of the subcategorisation relations between verbs and their nominalisations. We can also search for this nominalisation type in corpora using a lexically specified CQP-query. oe oy , a i i i i. ee ae
Table 5.32: Simplex and compound nominal predicates in the VF
Table 5.32: Simplex and compound nominal predicates in the VF
The specific queries are applied on the subcorpora acquired with the general queries described in section 5.3.1. For the specification of different predicate types, we use lexical and morpho-syntactic constraints which are integrated into the queries. More- over, for identification of some predicate types, e.g. nominalisations and compound nouns, we use morphological tools. In figure 5.11, we show the segment of the archi- tecture which contains procedures for the above described predicate classification.
The specific queries are applied on the subcorpora acquired with the general queries described in section 5.3.1. For the specification of different predicate types, we use lexical and morpho-syntactic constraints which are integrated into the queries. More- over, for identification of some predicate types, e.g. nominalisations and compound nouns, we use morphological tools. In figure 5.11, we show the segment of the archi- tecture which contains procedures for the above described predicate classification.
V1 contains those verbs, which subcategorise for both declaratives and interroga tives, dass-, w- and ob-clauses in our case. The V2 verbs take only interrogatives, bot! w-and ob-clauses in our study, whereas V3 includes verbs licensing declaratives onl} i.e. dass-clauses. To subclassify the extracted lists of verbs, we apply a set of regula expressions formulated in form of shell scripts, which allow us to group these verb according to the type of the complement clause they subcategorise for, as shown it table 5.34. Table 5.34: Subcategorisation features specific for different verb classes
V1 contains those verbs, which subcategorise for both declaratives and interroga tives, dass-, w- and ob-clauses in our case. The V2 verbs take only interrogatives, bot! w-and ob-clauses in our study, whereas V3 includes verbs licensing declaratives onl} i.e. dass-clauses. To subclassify the extracted lists of verbs, we apply a set of regula expressions formulated in form of shell scripts, which allow us to group these verb according to the type of the complement clause they subcategorise for, as shown it table 5.34. Table 5.34: Subcategorisation features specific for different verb classes
Table 5.38: The classification query to extract the Cl nominal compounds
Table 5.38: The classification query to extract the Cl nominal compounds
Table 5.39: Query segments with lexical constraints for compound subclassification
Table 5.39: Query segments with lexical constraints for compound subclassification
Figure 5.14: Extraction and subclassification of compounds 5.3.3.4 Subclassification of multiwords
Figure 5.14: Extraction and subclassification of compounds 5.3.3.4 Subclassification of multiwords
Table 5.40: Classification of multiword expressions A completely automatic distinction between these two types is difficult and no intended here (cf. e.g. (Fazly/Stevenson 2006) for work on the automatic separatior of idioms and (rather compositional) collocations). To identify the M3 multiwords we can apply a lexicon of nouns, w egorise for sentential complements. obviously identify “cranberry” lexica hich occur freely in corpora but do not subcat: To single out some of the M4-cases, we car items (see works on “cranberry” lexemes, e.g (Richter/Sailer 2002) and (Trawinski et al. 2008))1°, the rest of them can be identi fied as such. For the purpose of furt her discussion, we group the cases M1 and Mz together (cases with “inheritance” o f subcategorisation from the nominal part), a: well as M3 and M4 (“non-inheritance” cases). Examples of the four types of multiwords are given in table 5.40. The multiword expression in Aussicht stellen, extracted in Context 1, subcategorises for dass-clauses only. So does its nominal component Aussicht when extracted in Context 2. This multiword expression belongs to type M1. The expression in Erfahrung bringen can take both declarative (dass-clauses) and interrogative complements (w-/ob-clauses) in Context 2, whereas its nominal component takes declaratives only. Therefore, this multiword expression is of type M2. The multiword expression in Abrede stellen belongs to type M3 because its nominal component Abrede is a “cranberry” lexeme and does not appear outside a multiword, thus, cannot take any complements.
Table 5.40: Classification of multiword expressions A completely automatic distinction between these two types is difficult and no intended here (cf. e.g. (Fazly/Stevenson 2006) for work on the automatic separatior of idioms and (rather compositional) collocations). To identify the M3 multiwords we can apply a lexicon of nouns, w egorise for sentential complements. obviously identify “cranberry” lexica hich occur freely in corpora but do not subcat: To single out some of the M4-cases, we car items (see works on “cranberry” lexemes, e.g (Richter/Sailer 2002) and (Trawinski et al. 2008))1°, the rest of them can be identi fied as such. For the purpose of furt her discussion, we group the cases M1 and Mz together (cases with “inheritance” o f subcategorisation from the nominal part), a: well as M3 and M4 (“non-inheritance” cases). Examples of the four types of multiwords are given in table 5.40. The multiword expression in Aussicht stellen, extracted in Context 1, subcategorises for dass-clauses only. So does its nominal component Aussicht when extracted in Context 2. This multiword expression belongs to type M1. The expression in Erfahrung bringen can take both declarative (dass-clauses) and interrogative complements (w-/ob-clauses) in Context 2, whereas its nominal component takes declaratives only. Therefore, this multiword expression is of type M2. The multiword expression in Abrede stellen belongs to type M3 because its nominal component Abrede is a “cranberry” lexeme and does not appear outside a multiword, thus, cannot take any complements.
Table 5.43: Subcategorisation features of the three classes of -ung-nominalisations Table 5.42: Lexical constrains to obtain ung-nominalisations from corpora
Table 5.43: Subcategorisation features of the three classes of -ung-nominalisations Table 5.42: Lexical constrains to obtain ung-nominalisations from corpora
Table 5.46: Nominalisation-verb pairs after the morphological analysis
Table 5.46: Nominalisation-verb pairs after the morphological analysis
The system searches for base verbs subcategorising for dass, ob and w-clauses. The list of extracted verbs is used for the analysis of subcategorisation relations between base verbs their nominalisations. For this purpose base verbs are also classified into the three groups mentioned above (Vbasel, Vbase2, Vbase2). The classification pro- cedures do not differ from those applied for the subclassification of verbal predicates in general, which are described in section 5.3.3.1 above, cf. table 5.48 below.
The system searches for base verbs subcategorising for dass, ob and w-clauses. The list of extracted verbs is used for the analysis of subcategorisation relations between base verbs their nominalisations. For this purpose base verbs are also classified into the three groups mentioned above (Vbasel, Vbase2, Vbase2). The classification pro- cedures do not differ from those applied for the subclassification of verbal predicates in general, which are described in section 5.3.3.1 above, cf. table 5.48 below.
Figure 5.16: Architecture for extraction and classification of subcategorisation relations
Figure 5.16: Architecture for extraction and classification of subcategorisation relations
Summary The figures above show that our tools deliver a substantial number of subclause-taking nouns (over 15.000 types and over 59.000 tokens), which can be classified according to the type of the clause they subcategorise for. The obtained results allow us to compare the proportion of declarative and interrogative clauses occurring with nouns in VF, revealing that subclause-taking nouns show preferences for dass-clauses. The analysis of the three noun types classified by our tools confirms this tendency. The N1 and N3 nouns prove to be most frequent in our corpora. The N2 nominals, which do not allow for w-clauses, e.g. Motiv, w-/obappear to be not so frequent in the analysed corpora.
Summary The figures above show that our tools deliver a substantial number of subclause-taking nouns (over 15.000 types and over 59.000 tokens), which can be classified according to the type of the clause they subcategorise for. The obtained results allow us to compare the proportion of declarative and interrogative clauses occurring with nouns in VF, revealing that subclause-taking nouns show preferences for dass-clauses. The analysis of the three noun types classified by our tools confirms this tendency. The N1 and N3 nouns prove to be most frequent in our corpora. The N2 nominals, which do not allow for w-clauses, e.g. Motiv, w-/obappear to be not so frequent in the analysed corpora.
Table 6.4: The proportion of simplex and compound predicates occurring in VF
Table 6.4: The proportion of simplex and compound predicates occurring in VF
As the morphological tool provides us with information about the part of speech of compound elements, their nominal (NN) or deverbal (V) nature, we test the pro- portion of compound derivation types, see table 6.6. The data show that compounds whose parts have a deverbal nature, comprise about 46%, which is a considerable amount. We assume that the morphological structure of compounds can also influ- ence their subcategorisation behaviour, in particular in the cases when the non-head is the valency bearer (C2 and partially C3).
As the morphological tool provides us with information about the part of speech of compound elements, their nominal (NN) or deverbal (V) nature, we test the pro- portion of compound derivation types, see table 6.6. The data show that compounds whose parts have a deverbal nature, comprise about 46%, which is a considerable amount. We assume that the morphological structure of compounds can also influ- ence their subcategorisation behaviour, in particular in the cases when the non-head is the valency bearer (C2 and partially C3).
Table 6.7: The morphological analysis of compounds: subcategorisation types
Table 6.7: The morphological analysis of compounds: subcategorisation types
Table 6.8: Frequent deverbal non-heads of C2-compounds
Table 6.8: Frequent deverbal non-heads of C2-compounds
the underlying verbs are also common for other compound types, in which they can be either head (C1 and C3-1) or non-head C3-1 constituents, cf. table 6.11. We suppose that the unexpected subcategorisation behaviour of C2 and C3-1- compounds can be explained by the deverbal nature of their valency bearer. In the majority of such cases, the non-head constituent which determines the subcategorisa- tion of the whole compound is derived from a verb. As most nominalisations inherit subcategorisation properties of their base verbs, this behaviour proves to be expected. At the same time, about 38% of C3-1-compounds do not contain any deverbal constituents, e.g. Kritikpunkt (“criticism point (point of criticism)”, cf. table 6.7, and 29% of C2-compounds contain deverbal heads, e.g. Zielsetzung (“goal setting (aim)”) or Gesetzentwurf (“law draft (draft law)”) which is unexpected*. The ex-
the underlying verbs are also common for other compound types, in which they can be either head (C1 and C3-1) or non-head C3-1 constituents, cf. table 6.11. We suppose that the unexpected subcategorisation behaviour of C2 and C3-1- compounds can be explained by the deverbal nature of their valency bearer. In the majority of such cases, the non-head constituent which determines the subcategorisa- tion of the whole compound is derived from a verb. As most nominalisations inherit subcategorisation properties of their base verbs, this behaviour proves to be expected. At the same time, about 38% of C3-1-compounds do not contain any deverbal constituents, e.g. Kritikpunkt (“criticism point (point of criticism)”, cf. table 6.7, and 29% of C2-compounds contain deverbal heads, e.g. Zielsetzung (“goal setting (aim)”) or Gesetzentwurf (“law draft (draft law)”) which is unexpected*. The ex-
Table 6.11: C1 and C3-compounds containing deverbal non-heads vs. base verbs
Table 6.11: C1 and C3-compounds containing deverbal non-heads vs. base verbs
Table 6.14: Multiwords vs. nouns, which occur freely in context
Table 6.14: Multiwords vs. nouns, which occur freely in context
Table 6.15: Proportion of -ung-nominalisations in the VF We concentrate on the analysis of -ung-nominalisations. They comprise about 22% of all nominal predicates extracted in Vorfeld’, cf. table 6.15. The figures in ta- ble 6.16 show that -ung-nominalisations tend to take dass-subclauses in most cases. Furthermore, their preference for declarative complements is even greater, if com- pared to that of other nominal predicate types or nominal predicates general. Ung- nominalisations tend to subcategorise dass-clauses in approxiemately 77% of all cases extracted in Vorfeld, whereas other nominal predicates subcategorise for declaratives in roughly 61% of the obtained cases.
Table 6.15: Proportion of -ung-nominalisations in the VF We concentrate on the analysis of -ung-nominalisations. They comprise about 22% of all nominal predicates extracted in Vorfeld’, cf. table 6.15. The figures in ta- ble 6.16 show that -ung-nominalisations tend to take dass-subclauses in most cases. Furthermore, their preference for declarative complements is even greater, if com- pared to that of other nominal predicate types or nominal predicates general. Ung- nominalisations tend to subcategorise dass-clauses in approxiemately 77% of all cases extracted in Vorfeld, whereas other nominal predicates subcategorise for declaratives in roughly 61% of the obtained cases.
Table 6.16: Nominal predicates and their preferences for subclause types To analyse the proportion of nominal types (from Nung1 to Nung3), the clas- sification of which is described in section 5.3.4.1, we test the 290 most frequent -ung-nominalisation lemma types®. The figures show that similarly to the subclause- taking nouns in general (cf. section 6.1.1.1), the occurrence of the Nung1 and Nung2 nominalisations varies between types and tokens. Types of Nung3 predicates (ung- deverbals, which subcategorise for dass-clauses only) are more frequent than the types of N1 and N2 (about 56% vs. about22%). However, if compared in tokens, nominalisations belonging to type Nung1 (allowing for both dass and w-/ob-clauses) appear to be roughly 9% more frequent than those of Nung3 (53,07% vs. 43,66%). The number of occurrences for ung-nominalisations of type Nung2 appears to be very low (they comprise about 3% of the analysed nominalisations). The N1 nominals have more occurrences in the analysed corpora. Examples of ung-nominalisations classified according to Nungl to Nung3 are
Table 6.16: Nominal predicates and their preferences for subclause types To analyse the proportion of nominal types (from Nung1 to Nung3), the clas- sification of which is described in section 5.3.4.1, we test the 290 most frequent -ung-nominalisation lemma types®. The figures show that similarly to the subclause- taking nouns in general (cf. section 6.1.1.1), the occurrence of the Nung1 and Nung2 nominalisations varies between types and tokens. Types of Nung3 predicates (ung- deverbals, which subcategorise for dass-clauses only) are more frequent than the types of N1 and N2 (about 56% vs. about22%). However, if compared in tokens, nominalisations belonging to type Nung1 (allowing for both dass and w-/ob-clauses) appear to be roughly 9% more frequent than those of Nung3 (53,07% vs. 43,66%). The number of occurrences for ung-nominalisations of type Nung2 appears to be very low (they comprise about 3% of the analysed nominalisations). The N1 nominals have more occurrences in the analysed corpora. Examples of ung-nominalisations classified according to Nungl to Nung3 are
Base verbs and their classification The list of base verbs is generated from the list of ung-nominalisations, as described in section 5.3.4.2 above. We extract base verbs in VL and in passive? and obtain 8034 types and 22459 tokens. 1 The proportior between dass- and w/ob-clauses which occur with verbs shows that verbs do not have the same preferences for dass-clauses as their ung-nominalisations. that most extracted base verbs occur with w-/ob-clauses, as seen from The data show table 6.18. We assume that this phenomenon can be explained by contextual parameters of verbal predicates that occur with w-/ob-clauses in our corpora (cf. section 2.2.2.3 above). Such parameters as modality (usage with a modal verb), polarity (usage in negative context), as well as their mood (occurrence in interrogative vs. declarative context) can influence the choice of the subclause. The mentioned context parame- ters may switch thruth values of verbs, thus, the decision to take dass-clauses (which express propositions) or w-/ob-clauses, which express questions!° may depend on the context parameters mentioned above. To test this, we analyse the context pa- rameters of verbal predicates extracted from a corpus of ca. 105M tokens and calcu- late their occurrences with modal verbs, such as kdnnen (“can”), mtissen (“must”), wollen (“to want”), a semi-modal verb, e.g. lassen (“to let”), scheinen (“to seem”) or in a negative context, with a negative polarity item, such as nicht (“not”), niemand (“nobody”), nichts (“nothing”), kaum (“hardly/barely”), nie/niemals (“never”) and others!!. The proportion of dass- vs. w-/ob-clauses with the verbs, which occur in one of these contexts is shown in table 6.19. The figures show that w-/ob-clause-taking verbs comprise about 60% of all the
Base verbs and their classification The list of base verbs is generated from the list of ung-nominalisations, as described in section 5.3.4.2 above. We extract base verbs in VL and in passive? and obtain 8034 types and 22459 tokens. 1 The proportior between dass- and w/ob-clauses which occur with verbs shows that verbs do not have the same preferences for dass-clauses as their ung-nominalisations. that most extracted base verbs occur with w-/ob-clauses, as seen from The data show table 6.18. We assume that this phenomenon can be explained by contextual parameters of verbal predicates that occur with w-/ob-clauses in our corpora (cf. section 2.2.2.3 above). Such parameters as modality (usage with a modal verb), polarity (usage in negative context), as well as their mood (occurrence in interrogative vs. declarative context) can influence the choice of the subclause. The mentioned context parame- ters may switch thruth values of verbs, thus, the decision to take dass-clauses (which express propositions) or w-/ob-clauses, which express questions!° may depend on the context parameters mentioned above. To test this, we analyse the context pa- rameters of verbal predicates extracted from a corpus of ca. 105M tokens and calcu- late their occurrences with modal verbs, such as kdnnen (“can”), mtissen (“must”), wollen (“to want”), a semi-modal verb, e.g. lassen (“to let”), scheinen (“to seem”) or in a negative context, with a negative polarity item, such as nicht (“not”), niemand (“nobody”), nichts (“nothing”), kaum (“hardly/barely”), nie/niemals (“never”) and others!!. The proportion of dass- vs. w-/ob-clauses with the verbs, which occur in one of these contexts is shown in table 6.19. The figures show that w-/ob-clause-taking verbs comprise about 60% of all the
Table 6.25: “Non-inheritance” extracted from corpora verbs, e.g. erfahren - Erfahrung - Erfahrung haben - Erfahrung machen - in Erfahrung bringen (“to experience - experience - to bring into experience/to find out”). In ta- ble 6.26, we give several verb-nominalisation-multiword combinations, for which we indicate the type of clauses obtained from our corpora (ca. 1563M tokens). HIQICAle Ule LYPE OL CladUs€s OVLAlICA IPO) OUT COLPOTd (Cd. LOVOIVI LOKRCIIS). The sample results show that in most cases nominalisations both within and outside a multiword prove to “inherit” their subcategorisation properties from their base verbs. For instance, in entscheiden-Entscheidung-zu(de)r Entscheidung gelan- gen/kommen/stellen (“to decide-decision-to to come to/to bring to decision”), the base verb, its nominalisation and the multiword containing the nominalisations sub- categorise for both dass- and w-/ob-clauses. In abstimmen-Abstimmung-zur Abstim- mung kommen (“to vote - voting - to come to the vote”), both the verbs and their nominalisations take one of the subclause types and do not allow for the other. Table 6.27 llustrates the case in which the subcategorisation of the nominalisation neriwirrnng Anth wnithin a miaiiltitsnrd and fraaltz in enntavt diffare fram that nf tha
Table 6.25: “Non-inheritance” extracted from corpora verbs, e.g. erfahren - Erfahrung - Erfahrung haben - Erfahrung machen - in Erfahrung bringen (“to experience - experience - to bring into experience/to find out”). In ta- ble 6.26, we give several verb-nominalisation-multiword combinations, for which we indicate the type of clauses obtained from our corpora (ca. 1563M tokens). HIQICAle Ule LYPE OL CladUs€s OVLAlICA IPO) OUT COLPOTd (Cd. LOVOIVI LOKRCIIS). The sample results show that in most cases nominalisations both within and outside a multiword prove to “inherit” their subcategorisation properties from their base verbs. For instance, in entscheiden-Entscheidung-zu(de)r Entscheidung gelan- gen/kommen/stellen (“to decide-decision-to to come to/to bring to decision”), the base verb, its nominalisation and the multiword containing the nominalisations sub- categorise for both dass- and w-/ob-clauses. In abstimmen-Abstimmung-zur Abstim- mung kommen (“to vote - voting - to come to the vote”), both the verbs and their nominalisations take one of the subclause types and do not allow for the other. Table 6.27 llustrates the case in which the subcategorisation of the nominalisation neriwirrnng Anth wnithin a miaiiltitsnrd and fraaltz in enntavt diffare fram that nf tha
Table 6.26: Verbs vs. their nominalisations extracted from corpora
Table 6.26: Verbs vs. their nominalisations extracted from corpora
which often function as “negative context”!’. We tested 182 context types of some potentially negative verbs occurring in our data. In 74% of the extracted cases, they take w-/ob-clauses, cf. table 6.31. Nominal predicates occurring in VF are not modified by modal verbs, therefore hey are less influenced. Besides that, if used in VF, they cannot be directly negated by he negative dfinite article keine*°. However, multiword expressions, which consist of ‘nominal predicate and a support verb, behave syntactically like verbs, and thus, can e both, embedded under a modal verb or negated. Therefore, some nominalisations vithin a multiword show more preferences for w-/ob-subclauses than if they occur reely in context. For instance, the nominalisation Ahnung can subcategorise both or dass and w-/ob-clauses. However, if used in the Vorfeld, it shows preferences for leclarative sentences (71% vs. 29%), as seen in table 6.24 above. The multiword \hnung haben, on the contrary, prefers w-/ob-clauses (81% vs. 19%), and so does heir underlying verb ahnen. Our data show that both, the verb and the deverbal nultiword, if they subcategorise for an interrogative clause, occur with modal verbs rr negative polarity items in a considerable number of cases (ahnen, w-/ob in over 0% of the analysed cases, and Ahnung haben in 69%)". Titi 3 ak APTANA alates chews: Beam wren! wa cin ll ce acs fee ea wet ae me Cee: kk ee
which often function as “negative context”!’. We tested 182 context types of some potentially negative verbs occurring in our data. In 74% of the extracted cases, they take w-/ob-clauses, cf. table 6.31. Nominal predicates occurring in VF are not modified by modal verbs, therefore hey are less influenced. Besides that, if used in VF, they cannot be directly negated by he negative dfinite article keine*°. However, multiword expressions, which consist of ‘nominal predicate and a support verb, behave syntactically like verbs, and thus, can e both, embedded under a modal verb or negated. Therefore, some nominalisations vithin a multiword show more preferences for w-/ob-subclauses than if they occur reely in context. For instance, the nominalisation Ahnung can subcategorise both or dass and w-/ob-clauses. However, if used in the Vorfeld, it shows preferences for leclarative sentences (71% vs. 29%), as seen in table 6.24 above. The multiword \hnung haben, on the contrary, prefers w-/ob-clauses (81% vs. 19%), and so does heir underlying verb ahnen. Our data show that both, the verb and the deverbal nultiword, if they subcategorise for an interrogative clause, occur with modal verbs rr negative polarity items in a considerable number of cases (ahnen, w-/ob in over 0% of the analysed cases, and Ahnung haben in 69%)". Titi 3 ak APTANA alates chews: Beam wren! wa cin ll ce acs fee ea wet ae me Cee: kk ee
section 5.2 for details), built into our queries. The modelling of verb-final sentences depends to some extent on detailed models of nominal and prepositional phrases, and thus profits from additional preprocessing oy means of chunking or partial parsing. As our experiments show, for the Vorfeld cases, which are used in the extraction of nominal predicates, no partial parsing is needed. We compare figures of extraction patterns with and without NP and PP doundaries annotated (absolute frequency indicated under +chunks and -chunks in table 6.39). The chunked corpora provide slightly less data, but most of the cases found without the use of chunking (diff in table 6.39) proved to be true positives (TP, absolute figures and percentages). Evaluation of identification and classification of compounds We also analyse the morphological sorting procedure for a list of extracted nominal predicates (15687 types) and obtain 2037 types of nominal compounds, cf. figures in table 6.4 above. The evaluation of the sorting procedure shows that we achieve a precision of 94,2% and a recall of 95,7% in the automatic identification of nominal compounds. As the morphological tool provides us with information about the part of speech of com-
section 5.2 for details), built into our queries. The modelling of verb-final sentences depends to some extent on detailed models of nominal and prepositional phrases, and thus profits from additional preprocessing oy means of chunking or partial parsing. As our experiments show, for the Vorfeld cases, which are used in the extraction of nominal predicates, no partial parsing is needed. We compare figures of extraction patterns with and without NP and PP doundaries annotated (absolute frequency indicated under +chunks and -chunks in table 6.39). The chunked corpora provide slightly less data, but most of the cases found without the use of chunking (diff in table 6.39) proved to be true positives (TP, absolute figures and percentages). Evaluation of identification and classification of compounds We also analyse the morphological sorting procedure for a list of extracted nominal predicates (15687 types) and obtain 2037 types of nominal compounds, cf. figures in table 6.4 above. The evaluation of the sorting procedure shows that we achieve a precision of 94,2% and a recall of 95,7% in the automatic identification of nominal compounds. As the morphological tool provides us with information about the part of speech of com-
Figure 7.2: Examples of lexicon entries for C1 to C3 compounds
Figure 7.2: Examples of lexicon entries for C1 to C3 compounds
A.1 W-words in German A.2 Examples of Nominal Predicates: N1, N2, N3
A.1 W-words in German A.2 Examples of Nominal Predicates: N1, N2, N3
\.3 Examples of compound nouns: C2 and C3
\.3 Examples of compound nouns: C2 and C3
Tabelle 2: Dass-Nebensatze mit Verben oder MWAs in VL-Satzen Nomina (darunter auch Komposita, Nominalisierungen) werden im Vorfeld (VF) extrahiert. Das Vorfeld ist ein topologisches Feld vor dem finiten Verb in deutschen Deklarativsatzen. Wenn neben einem Nomen ein Nebensatz im Vorfeld vorkommt, dann kann dieser Nebensatz nur von diesem Nomen subkategorisiert werden, vgl. Tabelle 4.
Tabelle 2: Dass-Nebensatze mit Verben oder MWAs in VL-Satzen Nomina (darunter auch Komposita, Nominalisierungen) werden im Vorfeld (VF) extrahiert. Das Vorfeld ist ein topologisches Feld vor dem finiten Verb in deutschen Deklarativsatzen. Wenn neben einem Nomen ein Nebensatz im Vorfeld vorkommt, dann kann dieser Nebensatz nur von diesem Nomen subkategorisiert werden, vgl. Tabelle 4.
Related topics:
Clustering and Classification MethodsValency of nouns
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