PCFG models of linguistic tree representations
Computational Linguistics
A statistical parser for Czech
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Probabilistic parsing for German using sister-head dependencies
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Intricacies of Collins' Parsing Model
Computational Linguistics
Head-Driven Statistical Models for Natural Language Parsing
Computational Linguistics
Two statistical parsing models applied to the Chinese Treebank
CLPW '00 Proceedings of the second workshop on Chinese language processing: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 12
Noun phrase chunking in Hebrew: influence of lexical and morphological features
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Efficient parsing of highly ambiguous context-free grammars with bit vectors
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Integrated morphological and syntactic disambiguation for Modern Hebrew
COLING ACL '06 Proceedings of the 21st International Conference on computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
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Many state-of-the-art statistical parsers for English can be viewed as Probabilistic Context-Free Grammars (PCFGs) acquired from treebanks consisting of phrase-structure trees enriched with a variety of contextual, derivational (e.g., markovization) and lexical information. In this paper we empirically investigate the applicability and adequacy of the unlexicalized variety of such parsing models to Modern Hebrew, a Semitic language that differs in structure and characteristics from English. We show that contrary to experience with parsing the WSJ, the markovized, head-driven unlexicalized variety does not necessarily outperform plain PCFGs for Semitic languages. We demonstrate that enriching unlexicalized PCFGs with morphologically marked agreement features percolated up the parse tree (e.g., definiteness) outperforms plain PCFGs as well as a simple head-driven variation on the MH treebank. We further show that an (unlexicalized) head-driven variety enriched with the same features achieves even better performance. We conclude that morphologically rich languages introduce an additional dimension of parametrization that is orthogonal to the horizontal/ vertical dimensions proposed before [1] and its contribution is essential and complementary.