Partial Training for a Lexicalized-Grammar Parser

Proceedings of the Human Language Technology Conference of the North American Chapter of the ACL, pages 144–151, New York, June 2006. c⃝2006 Association for Computational Linguistics Partial Training for a Lexicalized-Grammar Parser Stephen Clark Oxford University Computing Laboratory Wolfson Building, Parks Road Oxford, OX1 3QD, UK stephen.clark@comlab.ox.ac.uk James R. Curran School of Information Technologies University of Sydney NSW 2006, Australia james@it.usyd.edu.au Abstract We propose a solution to the annotation bottleneck for statistical parsing, by ex- ploiting the lexicalized nature of Combi- natory Categorial Grammar (CCG). The parsing model uses predicate-argument dependencies for training, which are de- rived from sequences of CCG lexical cate- gories rather than full derivations. A sim- ple method is used for extracting depen- dencies from lexical category sequences, resulting in high precision, yet incomplete and noisy data. The dependency parsing model of Clark and Curran (2004b) is ex- tended to exploit this partial training data. Remarkably, the accuracy of the parser trained on data derived from category se- quences alone is only 1.3% worse in terms of F-score than the parser trained on com- plete dependency structures. 1 Introduction State-of-the-art statistical parsers require large amounts of hand-annotated training data, and are typically based on the Penn Treebank, the largest treebank available for English. Even robust parsers using linguistically sophisticated formalisms, such as TAG (Chiang, 2000), CCG (Clark and Curran, 2004b; Hockenmaier, 2003), HPSG (Miyao et al., 2004) and LFG (Riezler et al., 2002; Cahill et al., 2004), often use training data derived from the Penn Treebank. The labour-intensive nature of the tree- bank development process, which can take many years, creates a significant barrier for the develop- ment of parsers for new domains and languages. Previous work has attempted parser adaptation without relying on treebank data from the new do- main (Steedman et al., 2003; Lease and Charniak, 2005). In this paper we propose the use of anno- tated data in the new domain, but only partially an- notated data, which reduces the annotation effort re- quired (Hwa, 1999). We develop a parsing model which can be trained using partial data, by exploiting the properties of lexicalized grammar formalisms. The formalism we use is Combinatory Categorial Grammar (Steedman, 2000), together with a parsing model described in Clark and Curran (2004b) which we adapt for use with partial data. Parsing with Combinatory Categorial Grammar (CCG) takes place in two stages: first, CCG lexical categories are assigned to the words in the sentence, and then the categories are combined by the parser (Clark and Curran, 2004a). The lexical categories can be thought of as detailed part of speech tags and typically express subcategorization information. We exploit the fact that CCG lexical categories contain a lot of syntactic information, and can therefore be used for training a full parser, even though attach- ment information is not explicitly represented in a category sequence. Our partial training regime only requires sentences to be annotated with lexical cate- gories, rather than full parse trees; therefore the data can be produced much more …

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