Two-step TAG Parsing Revisited

Proceedings of the Language & …, 2005

Traitement Automatique des Langues, 2003

Tree Adjoining Grammar (TAG) is a useful formalism for describing the syntactic structure of natural languages. In practice, a large part of wide coverage TAGs is formed by trees that satisfy the restrictions imposed by Tree Insertion Grammar (TIG), a simpler formalism. This characteristic can be used to reduce the practical complexity of TAG parsing, applying the standard adjunction operation only in those cases in which the simpler cubic-time TIG adjunction cannot be applied. A major obstacle to this task is posed by the fact that simultaneous adjunctions are forbidden in TAG but they are allowed in TIG. In this article, we describe several algorithms for mixed parsing of TAG and TIG: a first one forbidding simultaneous adjunctions, a second one allowing this kind of adjunctions, and a third one which extends the second one to preserve the correct prefix property.

Lecture Notes in Computer Science, 2012

A POS-tagger can be used in front of a parser to reduce the number of combinations of possible dependency trees which, in the majority, give spurious analyses. In the paper we compare the results of the addition of three morphological taggers to the parser of the CDG Lab. The experimental results show that these models perform better than the model which do not use a morphological tagger at the cost of loosing some correct analyses. In fact, the adequacy of these solutions is mainly based on the compatibility between the lexical units defined by the taggers and the dependency grammar. Dependency-based representations have become increasingly popular in syntactic parsing, especially for languages that exhibit free or flexible word order, such 1 A LU may belong to several CDG classes.

Proceedings of the 11th International Conference on Parsing Technologies (IWPT'09)}

Computational linguistics, 1999

In this paper, we have proposed novel methods for robust parsing that integrate the flexibility of linguistically motivated lexical descriptions with the robustness of statistical techniques. Our thesis is that the computation of linguistic structure can be localized iflexical items are associated with rich descriptions (supertags) that impose complex constraints in a local context. The supertags are designed such that only those elements on which the lexical item imposes constraints appear within a given supertag. Further, each lexical item is associated with as many supertags as the number of different syntactic contexts in which the lexical item can appear. This makes the number of different descriptions for each lexical item much larger than when the descriptions are less complex, thus increasing the local ambiguity for a parser. But this local ambiguity can be resolved by using statistical distributions of supertag co-occurrences collected from a corpus of parses. We have explored these ideas in the context of the Lexicalized Tree-Adjoining Grammar (LTAG) framework. The supertags in LTAG combine both phrase structure information and dependency information in a single representation. Supertag disambiguation results in a representation that is effectively a parse (an almost parse), and the parser need "only" combine the individual supertags. This method of parsing can also be used to parse sentence fragments such as in spoken utterances where the disambiguated supertag sequence may not combine into a single structure.

1991

In this paper a bidirectional parser for Lexicalized Tree Adjoining Grammars will be presented. The algorithm takes advantage of a peculiar characteristic of Lexicalized TAGs, i.e. that each elementary tree is associated with a lexical item, called its anchor. The algorithm employs a mixed strategy: it works bottom-up from the lexical anchors and then expands (partial) analyses making top-down predictions. Even if such an algorithm does not improve tim worst-case time bounds of already known TAGs parsing methods, it could be relevant from the perspective of linguistic information processing, because it employs lexical information in a more direct way. -27 -Procedure 3 Move-dot-left Input A slate s=[

1982

Proceedings of the Joint Conference of the 47th …, 2009

This paper investigates the class of Tree-Tuple MCTAG with Shared Nodes, TT-MCTAG for short, an extension of Tree Adjoining Grammars that has been proposed for natural language processing, in particular for dealing with discontinuities and word order variation in languages such as German. It has been shown that the universal recognition problem for this formalism is NP-hard, but so far it was not known whether the class of languages generated by TT-MCTAG is included in PTIME. We provide a positive answer to this question, using a new characterization of TT-MCTAG.

2018

Information retrieval (IR) has become one of the most popular Natural Language Processing (NLP) applications. Part of speech (PoS) parsing and tagging plays an important role in IR systems. A broad range of PoS parsers and taggers tools have been proposed with the aim of helping to find a solution for the information retrieval problems, but most of these are tools based on generic NLP tags which do not capture domain-related information. In this research, we present a domain-specific parsing and tagging approach that uses not only generic PoS tags but also domain-specific PoS tags, grammatical rules, and domain knowledge. Experimental results show that our approach has a good level of accuracy when applying it to different domains.