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Distributional part-of-speech tagging
EACL '95 Proceedings of the seventh conference on European chapter of the Association for Computational Linguistics
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COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
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AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
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ALT '02 Proceedings of the 13th International Conference on Algorithmic Learning Theory
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AMTA '02 Proceedings of the 5th Conference of the Association for Machine Translation in the Americas on Machine Translation: From Research to Real Users
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Journal of Computer Science and Technology
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NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
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ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
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COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
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KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
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CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
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IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Data & Knowledge Engineering
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NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstration Session
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We present an architecture and an on-line learning algorithm and apply it to the problem of part-of-speech tagging. The architecture presented, SNOW, is a network of linear separators in the feature space, utilizing the Winnow update algorithm.Multiplicative weight-update algorithms such as Winnow have been shown to have exceptionally good behavior when applied to very high dimensional problems, and especially when the target concepts depend on only a small subset of the features in the feature space. In this paper we describe an architecture that utilizes this mistake-driven algorithm for multi-class prediction-selecting the part of speech of a word. The experimental analysis presented here provides more evidence to that these algorithms are suitable for natural language problems.The algorithm used is an on-line algorithm: every example is used by the algorithm only once, and is then discarded. This has significance in terms of efficiency, as well as quick adaptation to new contexts.We present an extensive experimental study of our algorithm under various conditions; in particular, it is shown that the algorithm performs comparably to the best known algorithms for POS.