Curbing Feature Coding: Strictly Local Feature Assignment

Graf (2017) warns that every syntactic formalism faces a severe overgeneration problem because of the hidden power of subcategorization. Any constraint definable in monadic second-order logic can be compiled into the category system so that it is indirectly enforced as part of subcategorization. Not only does this kind of feature coding deprive syntactic proposals of their empirical bite, it also undermines computational efforts to limit syntactic formalisms via subregular complexity. This paper presents a subregular solution to feature coding. Instead of features being a cheap resource that comes for free, features must be assigned by a transduction. In particular, category features must be assigned by an input strictly local (ISL) tree-to-tree transduction, defined here for the first time. The restriction to ISL transductions correctly rules out various deviant category systems

Complexity of Lexical Descriptions and its Relevance to Partial Parsing

In this dissertation, 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 if lexical items are associated with rich descriptions (supertags) that impose complex constraints in a local context. However, increasing the complexity of descriptions makes the number of different descriptions for each lexical item much larger and hence increases the local ambiguity for a parser. This local ambiguity can be resolved by using supertag co-occurrence statistics collected from parsed corpora. We have explored these ideas in the context of Lexicalized Tree-Adjoining Grammar (LTAG) framework wherein supertag disambiguation provides a representation that is an almost parse. We have used the disambiguated supertag sequence in conjunction with a lightweight dependency analyzer to compute noun groups, verb groups, dependency linkages and even partial parses. We have shown that a trigram-based supertagger achieves an accuracy of 92.1‰ on Wall Street Journal (WSJ) texts. Furthermore, we have shown that the lightweight dependency analysis on the output of the supertagger identifies 83‰ of the dependency links accurately. We have exploited the representation of supertags with Explanation-Based Learning to improve parsing effciency. In this approach, parsing in limited domains can be modeled as a Finite-State Transduction. We have implemented such a system for the ATIS domain which improves parsing eciency by a factor of 15. We have used the supertagger in a variety of applications to provide lexical descriptions at an appropriate granularity. In an information retrieval application, we show that the supertag based system performs at higher levels of precision compared to a system based on part-of-speech tags. In an information extraction task, supertags are used in specifying extraction patterns. For language modeling applications, we view supertags as syntactically motivated class labels in a class-based language model. The distinction between recursive and non-recursive supertags is exploited in a sentence simplification application

Ontologies and Information Extraction

This report argues that, even in the simplest cases, IE is an ontology-driven process. It is not a mere text filtering method based on simple pattern matching and keywords, because the extracted pieces of texts are interpreted with respect to a predefined partial domain model. This report shows that depending on the nature and the depth of the interpretation to be done for extracting the information, more or less knowledge must be involved. This report is mainly illustrated in biology, a domain in which there are critical needs for content-based exploration of the scientific literature and which becomes a major application domain for IE

Tabulation for multi-purpose partial parsing

Efficient partial parsing systems (chunkers) are urgently required by various natural language application areas as these parsers always produce partially parsed text even when the text does not fully fit existing lexica and grammars. Availability of partially parsed corpora is absolutely necessary for extracting various kinds of information that may then be fed into those systems, increasing their processing power. In this paper, we propose an efficient partial parsing scheme based on chart parsing that is flexible enough to support both normal parsing tasks and diagnosis in previously obtained partial parses of possible causes (kinds of faults) that led to those partial parses instead of complete parses. Through the use of the built-in tabulation capabilites of the DyALog system, we implemented a partial parser that runs as fast as the best non-deterministic parsers. In this paper we ellaborate on the implementation of two different grammar formalisms: Definite Clause Grammars (DCG) extended with head declarations and Bound Movement Grammars (BMG)

Proceedings

Proceedings of the NODALIDA 2011 Workshop Constraint Grammar Applications. Editors: Eckhard Bick, Kristin Hagen, Kaili Müürisep, Trond Trosterud. NEALT Proceedings Series, Vol. 14 (2011), vi+69 pp. © 2011 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/19231