Multi-Tagging for Lexicalized-Grammar Parsing

Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 697–704, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Multi-Tagging for Lexicalized-Grammar Parsing James R. Curran School of IT University of Sydney NSW 2006, Australia james@it.usyd.edu.au Stephen Clark Computing Laboratory Oxford University Wolfson Building Parks Road Oxford, OX1 3QD, UK sclark@comlab.ox.ac.uk David Vadas School of IT University of Sydney NSW 2006, Australia dvadas1@it.usyd.edu.au Abstract With performance above 97% accuracy for newspaper text, part of speech (POS) tag- ging might be considered a solved prob- lem. Previous studies have shown that allowing the parser to resolve POS tag ambiguity does not improve performance. However, for grammar formalisms which use more fine-grained grammatical cate- gories, for example TAG and CCG, tagging accuracy is much lower. In fact, for these formalisms, premature ambiguity resolu- tion makes parsing infeasible. We describe a multi-tagging approach which maintains a suitable level of lexical category ambiguity for accurate and effi- cient CCG parsing. We extend this multi- tagging approach to the POS level to over- come errors introduced by automatically assigned POS tags. Although POS tagging accuracy seems high, maintaining some POS tag ambiguity in the language pro- cessing pipeline results in more accurate CCG supertagging. 1 Introduction State-of-the-art part of speech (POS) tagging ac- curacy is now above 97% for newspaper text (Collins, 2002; Toutanova et al., 2003). One pos- sible conclusion from the POS tagging literature is that accuracy is approaching the limit, and any remaining improvement is within the noise of the Penn Treebank training data (Ratnaparkhi, 1996; Toutanova et al., 2003). So why should we continue to work on the POS tagging problem? Here we give two reasons. First, for lexicalized grammar formalisms such as TAG and CCG, the tagging problem is much harder. Second, any errors in POS tagger output, even at 97% acuracy, can have a significant impact on components further down the language processing pipeline. In previous work we have shown that us- ing automatically assigned, rather than gold stan- dard, POS tags reduces the accuracy of our CCG parser by almost 2% in dependency F-score (Clark and Curran, 2004b). CCG supertagging is much harder than POS tag- ging because the CCG tag set consists of fine- grained lexical categories, resulting in a larger tag set – over 400 CCG lexical categories compared with 45 Penn Treebank POS tags. In fact, using a state-of-the-art tagger as a front end to a CCG parser makes accurate parsing infeasible because of the high supertagging error rate. Our solution is to use multi-tagging, in which a CCG supertagger can potentially assign more than one lexical category to a word. In this paper we significantly improve our earlier ap- proach (Clark and Curran, 2004a) by adapting the forward-backward algorithm to a Maximum En- tropy tagger, which is used to calculate a proba- bility distribution over lexical categories for each word. This distribution is used to assign one or more categories to each word (Charniak et al., 1996). …

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