Robust parsing using a hidden Markov model

Authors:
Wide R. Hogenhout;Yuji Matsumoto
Affiliations:
Nara Institute of Science and Technology;Nara Institute of Science and Technology
Venue:
FSMNLP '09 Proceedings of the International Workshop on Finite State Methods in Natural Language Processing
Year:
1998

Citing 6
Cited 0

Building a large annotated corpus of English: the penn treebank

Computational Linguistics - Special issue on using large corpora: II
Three generative, lexicalised models for statistical parsing

ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Statistical decision-tree models for parsing

ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Disambiguation of super parts of speech (or supertags): almost parsing

COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Error-tolerant tree matching

COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
Statistical parsing with a context-free grammar and word statistics

AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Recent approaches to statistical parsing include those that estimate an approximation of a stochastic, lexicalized grammar directly from a treebank and others that rebuild trees with a number of tree-constructing operators, which are applied in order according to a stochastic model when parsing a sentence. In this paper we take an entirely different approach to statistical parsing, as we propose a method for parsing using a Hidden Markov Model. We describe the stochastic model and the tree construction procedure, and we report results on the Wall Street Journal Corpus.