Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Coping with ambiguity and unknown words through probabilistic models
Computational Linguistics - Special issue on using large corpora: II
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Probabilistic CFG with latent annotations
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Learning accurate, compact, and interpretable tree annotation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Automated multiword expression prediction for grammar engineering
MWE '06 Proceedings of the Workshop on Multiword Expressions: Identifying and Exploiting Underlying Properties
Exploiting named entity classes in CCG surface realization
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Improving generative statistical parsing with semi-supervised word clustering
IWPT '09 Proceedings of the 11th International Conference on Parsing Technologies
Can recognising multiword expressions improve shallow parsing?
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Multiword expressions in statistical dependency parsing
SPMRL '11 Proceedings of the Second Workshop on Statistical Parsing of Morphologically Rich Languages
Combining compound recognition and PCFG-LA parsing with word lattices and conditional random fields
ACM Transactions on Speech and Language Processing (TSLP) - Special issue on multiword expressions: From theory to practice and use, part 2
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In this paper we present preliminary experiments that aim to reduce lexical data sparsity in statistical parsing by exploiting information about named entities. Words in the WSJ corpus are mapped to named entity clusters and a latent variable constituency parser is trained and tested on the transformed corpus. We explore two different methods for mapping words to entities, and look at the effect of mapping various subsets of named entity types. Thus far, results show no improvement in parsing accuracy over the best baseline score; we identify possible problems and outline suggestions for future directions.