Coping with ambiguity and unknown words through probabilistic models
Computational Linguistics - Special issue on using large corpora: II
Unsupervised learning of word-category guessing rules
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Part-of-speech tagging with neural networks
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Email classification for contact centers
Proceedings of the 2003 ACM symposium on Applied computing
Automatic rule induction for unknown-word guessing
Computational Linguistics
POS disambiguation and unknown word guessing with decision trees
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
Automatic thesaurus generation through multiple filtering
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Automatic lexical acquisition from raw corpora: an application to Russian
MorphSlav '03 Proceedings of the 2003 EACL Workshop on Morphological Processing of Slavic Languages
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One of the problems in part-of-speech tagging of real-word texts is that of unknown to the lexicon words. In (Mikheev, 1996), a technique for fully unsupervised statistical acquisition of rules which guess possible parts-of-speech for unknown words was proposed. One of the over-simplification assumed by this learning technique was the acquisition of morphological rules which obey only simple concatenative regularities of the main word with an affix. In this paper we extend this technique to the non-concatenative cases of suffixation and assess the gain in the performance.