Building Deep Dependency Structures using a Wide-Coverage CCG Parser

Building Deep Dependency Structures with a Wide-Coverage CCG Parser Stephen Clark, Julia Hockenmaier and Mark Steedman Division of Informatics University of Edinburgh Edinburgh EH8 9LW, UK � stephenc, julia, steedman � @cogsci.ed.ac.uk Abstract This paper describes a wide-coverage sta- tistical parser that uses Combinatory Cat- egorial Grammar (CCG) to derive de- pendency structures. The parser differs from most existing wide-coverage tree- bank parsers in capturing the long-range dependencies inherent in constructions such as coordination, extraction, raising and control, as well as the standard local predicate-argument dependencies. A set of dependency structures used for train- ing and testing the parser is obtained from a treebank of CCG normal-form deriva- tions, which have been derived (semi-) au- tomatically from the Penn Treebank. The parser correctly recovers over 80% of la- belled dependencies, and around 90% of unlabelled dependencies. 1 Introduction Most recent wide-coverage statistical parsers have used models based on lexical dependencies (e.g. Collins (1999), Charniak (2000)). However, the de- pendencies are typically derived from a context-free phrase structure tree using simple head percolation heuristics. This approach does not work well for the long-range dependencies involved in raising, con- trol, extraction and coordination, all of which are common in text such as the Wall Street Journal. Chiang (2000) uses Tree Adjoining Grammar as an alternative to context-free grammar, and here we use another “mildly context-sensitive” for- malism, Combinatory Categorial Grammar (CCG, Steedman (2000)), which arguably provides the most linguistically satisfactory account of the de- pendencies inherent in coordinate constructions and extraction phenomena. The potential advantage from using such an expressive grammar is to facili- tate recovery of such unbounded dependencies. As well as having a potential impact on the accuracy of the parser, recovering such dependencies may make the output more useful. CCG is unlike other formalisms in that the stan- dard predicate-argument relations relevant to inter- pretation can be derived via extremely non-standard surface derivations. This impacts on how best to de- fine a probability model for CCG, since the “spuri- ous ambiguity” of CCG derivations may lead to an exponential number of derivations for a given con- stituent. In addition, some of the spurious deriva- tions may not be present in the training data. One solution is to consider only the normal-form (Eis- ner, 1996a) derivation, which is the route taken in Hockenmaier and Steedman (2002b).1 Another problem with the non-standard surface derivations is that the standard PARSEVAL per- formance measures over such derivations are unin- formative (Clark and Hockenmaier, 2002). Such measures have been criticised by Lin (1995) and Carroll et al. (1998), who propose recovery of head- dependencies characterising predicate-argument re- lations as a more meaningful measure. If the end-result of parsing is interpretable predicate-argument structure or the related depen- dency structure, then the question arises: why build derivation structure at all? A CCG parser can directly build derived structures, including long- 1Another, more speculative, possibility is to treat the alter- native derivations as hidden and apply the EM algorithm. Computational Linguistics (ACL), Philadelphia, July 2002, pp. 327-334. …

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