A Machine Learning Approach to POS Tagging
Machine Learning
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ICG! '96 Proceedings of the 3rd International Colloquium on Grammatical Inference: Learning Syntax from Sentences
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The Journal of Machine Learning Research
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Computational Linguistics - Special issue on using large corpora: II
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Computational Linguistics
Improving accuracy in word class tagging through the combination of machine learning systems
Computational Linguistics
TnT: a statistical part-of-speech tagger
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
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ANLC '88 Proceedings of the second conference on Applied natural language processing
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ANLC '94 Proceedings of the fourth conference on Applied natural language processing
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ANLC '92 Proceedings of the third conference on Applied natural language processing
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ANLC '92 Proceedings of the third conference on Applied natural language processing
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EACL '95 Proceedings of the seventh conference on European chapter of the Association for Computational Linguistics
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COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
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COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
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ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
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COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
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ACM SIGKDD Explorations Newsletter - Natural language processing and text mining
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Information Processing Letters
Adding morphological information to a connectionist part-of-speech tagger
CAEPIA'09 Proceedings of the Current topics in artificial intelligence, and 13th conference on Spanish association for artificial intelligence
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We introduce a simple method to build Lexicalized Hidden Markov Models (L-HMMs) for improving the precision of part-of-speech tagging. This technique enriches the contextual Language Model taking into account a set of selected words empirically obtained. The evaluation was conducted with different lexicalization criteria on the Penn Treebank corpus using the TnT tagger. This lexicalization obtained about a 6% reduction of the tagging error, on an unseen data test, without reducing the efficiency of the system. We have also studied how the use of linguistic resources, such as dictionaries and morphological analyzers, improves the tagging performance. Furthermore, we have conducted an exhaustive experimental comparison that shows that Lexicalized HMMs yield results which are better than or similar to other state-of-the-art part-of-speech tagging approaches. Finally, we have applied Lexicalized HMMs to the Spanish corpus LexEsp.