Lexicalization in Crosslinguistic Probabilistic Parsing: The Case of French

Proceedings of the 43rd Annual Meeting of the ACL, pages 306–313, Ann Arbor, June 2005. c⃝2005 Association for Computational Linguistics Lexicalization in Crosslinguistic Probabilistic Parsing: The Case of French Abhishek Arun and Frank Keller School of Informatics, University of Edinburgh 2 Buccleuch Place, Edinburgh EH8 9LW, UK a.arun@sms.ed.ac.uk, keller@inf.ed.ac.uk Abstract This paper presents the first probabilistic parsing results for French, using the re- cently released French Treebank. We start with an unlexicalized PCFG as a base- line model, which is enriched to the level of Collins’ Model 2 by adding lexical- ization and subcategorization. The lexi- calized sister-head model and a bigram model are also tested, to deal with the flat- ness of the French Treebank. The bigram model achieves the best performance: 81% constituency F-score and 84% de- pendency accuracy. All lexicalized mod- els outperform the unlexicalized baseline, consistent with probabilistic parsing re- sults for English, but contrary to results for German, where lexicalization has only a limited effect on parsing performance. 1 Introduction This paper brings together two strands of research that have recently emerged in the field of probabilis- tic parsing: crosslinguistic parsing and lexicalized parsing. Interest in parsing models for languages other than English has been growing, starting with work on Czech (Collins et al., 1999) and Chinese (Bikel and Chiang, 2000; Levy and Manning, 2003). Probabilistic parsing for German has also been ex- plored by a range of authors (Dubey and Keller, 2003; Schiehlen, 2004). In general, these authors have found that existing lexicalized parsing models for English (e.g., Collins 1997) do not straightfor- wardly generalize to new languages; this typically manifests itself in a severe reduction in parsing per- formance compared to the results for English. A second recent strand in parsing research has dealt with the role of lexicalization. The conven- tional wisdom since Magerman (1995) has been that lexicalization substantially improves performance compared to an unlexicalized baseline model (e.g., a probabilistic context-free grammar, PCFG). How- ever, this has been challenged by Klein and Man- ning (2003), who demonstrate that an unlexicalized model can achieve a performance close to the state of the art for lexicalized models. Furthermore, Bikel (2004) provides evidence that lexical information (in the form of bi-lexical dependencies) only makes a small contribution to the performance of parsing models such as Collins’s (1997). The only previous authors that have directly ad- dressed the role of lexicalization in crosslinguistic parsing are Dubey and Keller (2003). They show that standard lexicalized models fail to outperform an unlexicalized baseline (a vanilla PCFG) on Ne- gra, a German treebank (Skut et al., 1997). They attribute this result to two facts: (a) The Negra an- notation assumes very flat trees, which means that Collins-style head-lexicalization fails to pick up the relevant information from non-head nodes. (b) Ger- man allows flexible word order, which means that standard parsing models based on context free gram- mars perform poorly, as they fail to generalize over different positions of the same constituent. As it stands, Dubey and Keller’s (2003) …

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