Robust parsing using a hidden Markov model
| Autor: | Yuji Matsumoto, Wide R. Hogenhout |
|---|---|
| Rok vydání: | 1998 |
| Předmět: |
Parsing
business.industry Computer science Stochastic modelling Computer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing) computer.software_genre Top-down parsing TheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGES Parser combinator S-attributed grammar Statistical parsing Artificial intelligence Hidden Markov model business computer Natural language processing Bottom-up parsing |
| Zdroj: | Proceedings of the International Workshop on Finite State Methods in Natural Language Processing - FSMNLP '09. |
| DOI: | 10.3115/1611533.1611537 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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