- Abstract:
-
This paper compares a number of generative probability models for a widecoverage Combinatory Categorial Grammar (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 linguistically less expressive grammar. In contrast to Gildea (2001), we find a significant improvement from modeling wordword dependencies.
- Links To Paper
- ACL Anthology (publisher)
- Personal web page
- Bibtex format
- @InProceedings{EDI-INF-RR-0818,
- author = {
Julia Hockenmaier
and Mark Steedman
},
- title = {Generative Models for Statistical Parsing with Combinatory Grammars},
- book title = {Proceedings of ACL 2002 (Meeting of the Association for Computational Linguistics)},
- publisher = {ACL},
- year = 2002,
- pages = {335-342},
- doi = {10.3115/1073083.1073139},
- url = {http://acl.ldc.upenn.edu/P/P02/P02-1043.pdf},
- }
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