Automatic stochastic tagging of natural language texts
Computational Linguistics
Tagging English text with a probabilistic model
Computational Linguistics
A stochastic parts program and noun phrase parser for unrestricted text
ANLC '88 Proceedings of the second conference on Applied natural language processing
A practical part-of-speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
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
A stochastic language model using dependency and its improvement by word clustering
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Exploiting syntactic structure for language modeling
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Using decision trees to construct a practical parser
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
A new statistical parser based on bigram lexical dependencies
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Three new probabilistic models for dependency parsing: an exploration
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Dependency parsing with an extended finite state approach
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Augmenting a hidden Markov model for phrase-dependent word tagging
HLT '89 Proceedings of the workshop on Speech and Natural Language
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
A stochastic parser based on an SLM with arboreal context trees
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Sequential dependency analysis for online spontaneous speech processing
Speech Communication
A unified single scan algorithm for Japanese base phrase chunking and dependency parsing
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
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In this paper, we present a stochastic language model using dependency. This model considers a sentence as a word sequence and predicts each word from left to right. The history at each step of prediction is a sequence of partial parse trees covering the preceding words. First our model predicts the partial parse trees which have a dependency relation with the next word among them and then predicts the next word from only the trees which have a dependency relation with the next word. Our model is a generative stochastic model, thus this can be used not only as a parser but also as a language model of a speech recognizer. In our experiment, we prepared about 1,000 syntactically annotated Japanese sentences extracted from a financial newspaper and estimated the parameters of our model. We built a parser based on our model and tested it on approximately 100 sentences of the same newspaper. The accuracy of the dependency relation was 89.9%, the highest accuracy level obtained by Japanese stochastic parsers.