Parsing the Wall Street Journal using a Lexical-Functional Grammar and Discriminative Estimation Techniques

Parsing the Wall Street Journal using a Lexical-Functional Grammar and Discriminative Estimation Techniques Stefan Riezler Tracy H. King Ronald M. Kaplan Palo Alto Research Center Palo Alto Research Center Palo Alto Research Center Palo Alto, CA 94304 Palo Alto, CA 94304 Palo Alto, CA 94304 riezler@parc.com thking@parc.com kaplan@parc.com Richard Crouch John T. Maxwell III Mark Johnson Palo Alto Research Center Palo Alto Research Center Brown University Palo Alto, CA 94304 Palo Alto, CA 94304 Providence, RI 02912 crouch@parc.com maxwell@parc.com mj@cs.brown.edu Abstract We present a stochastic parsing system consisting of a Lexical-Functional Gram- mar (LFG), a constraint-based parser and a stochastic disambiguation model. We re- port on the results of applying this sys- tem to parsing the UPenn Wall Street Journal (WSJ) treebank. The model com- bines full and partial parsing techniques to reach full grammar coverage on unseen data. The treebank annotations are used to provide partially labeled data for dis- criminative statistical estimation using ex- ponential models. Disambiguation perfor- mance is evaluated by measuring matches of predicate-argument relations on two distinct test sets. On a gold standard of manually annotated f-structures for a sub- set of the WSJ treebank, this evaluation reaches 79% F-score. An evaluation on a gold standard of dependency relations for Brown corpus data achieves 76% F-score. 1 Introduction Statistical parsing using combined systems of hand- coded linguistically fine-grained grammars and stochastic disambiguation components has seen con- siderable progress in recent years. However, such at- tempts have so far been confined to a relatively small scale for various reasons. Firstly, the rudimentary character of functional annotations in standard tree- banks has hindered the direct use of such data for statistical estimation of linguistically fine-grained statistical parsing systems. Rather, parameter esti- mation for such models had to resort to unsupervised techniques (Bouma et al., 2000; Riezler et al., 2000), or training corpora tailored to the specific grammars had to be created by parsing and manual disam- biguation, resulting in relatively small training sets of around 1,000 sentences (Johnson et al., 1999). Furthermore, the effort involved in coding broad- coverage grammars by hand has often led to the spe- cialization of grammars to relatively small domains, thus sacrificing grammar coverage (i.e. the percent- age of sentences for which at least one analysis is found) on free text. The approach presented in this paper is a first attempt to scale up stochastic parsing systems based on linguistically fine-grained hand- coded grammars to the UPenn Wall Street Journal (henceforth WSJ) treebank (Marcus et al., 1994). The problem of grammar coverage, i.e. the fact that not all sentences receive an analysis, is tack- led in our approach by an extension of a full- fledged Lexical-Functional Grammar (LFG) and a constraint-based parser with partial parsing tech- niques. In the absence of a complete parse, a so- called “FRAGMENT grammar” allows the input to be analyzed as a sequence of well-formed chunks. The set of fragment parses is then chosen on the basis of a fewest-chunk method. With this combination of full and …

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