Generative Models for Statistical Parsing with Combinatory Categorial Grammar

Generative Models for Statistical Parsing with Combinatory Categorial Grammar Julia Hockenmaier and Mark Steedman Division of Informatics University of Edinburgh Edinburgh EH8 9LW, United Kingdom fjulia, steedman g@cogsci.ed.ac.uk Abstract This paper compares a number of gen- erative probability models for a wide- coverage Combinatory Categorial Gram- mar (CCG) parser. These models are trained and tested on a corpus obtained by translating the Penn Treebank trees into CCG normal-form derivations. According to an evaluation of unlabeled word-word dependencies, our best model achieves a performance of 89.9%, comparable to the figures given by Collins (1999) for a lin- guistically less expressive grammar. In contrast to Gildea (2001), we find a signif- icant improvement from modeling word- word dependencies. 1 Introduction The currently best single-model statistical parser (Charniak, 1999) achieves Parseval scores of over 89% on the Penn Treebank. However, the grammar underlying the Penn Treebank is very permissive, and a parser can do well on the standard Parseval measures without committing itself on certain se- mantically significant decisions, such as predicting null elements arising from deletion or movement. The potential benefit of wide-coverage parsing with CCG lies in its more constrained grammar and its simple and semantically transparent capture of ex- traction and coordination. We present a number of models over syntac- tic derivations of Combinatory Categorial Grammar (CCG, see Steedman (2000) and Clark et al. (2002), this conference, for introduction), estimated from and tested on a translation of the Penn Treebank to a corpus of CCG normal-form derivations. CCG grammars are characterized by much larger category sets than standard Penn Treebank grammars, distin- guishing for example between many classes of verbs with different subcategorization frames. As a re- sult, the categorial lexicon extracted for this purpose from the training corpus has 1207 categories, com- pared with the 48 POS-tags of the Penn Treebank. On the other hand, grammar rules in CCG are lim- ited to a small number of simple unary and binary combinatory schemata such as function application and composition. This results in a smaller and less overgenerating grammar than standard PCFGs (ca. 3,000 rules when instantiated with the above cate- gories in sections 02-21, instead of >12,400 in the original Treebank representation (Collins, 1999)). 2 Evaluating a CCG parser Since CCG produces unary and binary branching trees with a very fine-grained category set, CCG Parseval scores cannot be compared with scores of standard Treebank parsers. Therefore, we also evaluate performance using a dependency evalua- tion reported by Collins (1999), which counts word- word dependencies as determined by local trees and their labels. According to this metric, a local tree with parent node P, head daughter H and non-head daughter S (and position of S relative to P, ie. left or right, which is implicit in CCG categories) de- fines a hP ;H ;S i dependency between the head word of S, wS, and the head word of H, wH. This measure is neutral with respect to the branching factor. Fur- thermore, as noted by Hockenmaier (2001), it does not …

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