A multi-neuro tagger using variable lengths of contexts

Authors:
Qing Ma;Hitoshi Isahara
Affiliations:
Communications Research Laboratory, Ministry of Posts and Telecommunications, Kobe, Japan;Communications Research Laboratory, Ministry of Posts and Telecommunications, Kobe, Japan
Venue:
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Year:
1998

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Abstract

This paper presents a multi-neuro tagger that uses variable lengths of contexts and weighted inputs (with information gains) for part of speech tagging. Computer experiments show that it has a correct rate of over 94% for tagging ambiguous words when a small Thai corpus with 22,311 ambiguous words is used for training. This result is better than any of the results obtained using the single-neuro taggers with fixed but different lengths of contexts, which indicates that the multi-neuro tagger can dynamically find a suitable length of contexts in tagging.