Computational Linguistics
The power of amnesia: learning probabilistic automata with variable memory length
Machine Learning - Special issue on COLT '94
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
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
Inside-outside reestimation from partially bracketed corpora
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
A stochastic parser based on a structural word prediction model
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Immediate-head parsing for language models
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Japanese dependency structure analysis based on support vector machines
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
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 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 parser based on a stochastic structured language model (SLM) with a flexible history reference mechanism. An SLM is an alternative to an n-gram model as a language model for a speech recognizer. The advantage of an SLM against an n-gram model is the ability to return the structure of a given sentence. Thus SLMs are expected to play an important part in spoken language understanding systems. The current SLMs refer to a fixed part of the history for prediction just like an n-gram model. We introduce a flexible history reference mechanism called an ACT (arboreal context tree; an extension of the context tree to tree-shaped histories) and describe a parser based on an SLM with ACTs. In the experiment, we built an SLM-based parser with a fixed history and one with ACTs, and compared their parsing accuracies. The accuracy of our parser was 92.8%, which was higher than that for the parser with the fixed history (89.8%). This result shows that the flexible history reference mechanism improves the parsing ability of an SLM, which has great importance for language understanding.