Natural Language Processing: The Plnlp Approach
Natural Language Processing: The Plnlp Approach
A simple approach to building ensembles of Naive Bayesian classifiers for word sense disambiguation
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Three heads are better than one
ANLC '94 Proceedings of the fourth conference on Applied natural language processing
Japanese dependency structure analysis based on maximum entropy models
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
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
Classifier combination for improved lexical disambiguation
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Improving data driven wordclass tagging by system combination
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
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
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
Syntactic parser combination for improved dependency analysis
ROMAND '04 Proceedings of the 3rd Workshop on RObust Methods in Analysis of Natural Language Data
Improving parsing accuracy by combining diverse dependency parsers
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
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This paper explores two directions for the next step beyond the state of the art of statistical parsing: probabilistic partial parsing and committee-based decision making. Probabilistic partial parsing is a probabilistic extension of the existing notion of partial parsing, which enables fine-grained arbitrary choice on the trade-off between accuracy and coverage. Committee-based decision making is to combine the out-puts from different systems to make a better decision. While various committee-based techniques for NLP have recently been investigated, they would need to be further extended so as to be applicable to probabilistic partial parsing. Aiming at this coupling, this paper gives a general framework to committee-based decision making, which consists of a set of weighting functions and a combination function, and discusses how it can be coupled with probabilistic partial parsing. Our experiments have so far been producing promising results.