NDLI: Chinese named entity recognition using lexicalized HMMs

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Chinese named entity recognition using lexicalized HMMs

Content Provider	ACM Digital Library
Author	Fu, Guohong Luke, Kang-Kwong
Abstract	This paper presents a lexicalized HMM-based approach to Chinese named entity recognition (NER). To tackle the problem of unknown words, we unify unknown word identification and NER as a single tagging task on a sequence of known words. To do this, we first employ a known-word bigram-based model to segment a sentence into a sequence of known words, and then apply the uniformly lexicalized HMMs to assign each known word a proper hybrid tag that indicates its pattern in forming an entity and the category of the formed entity. Our system is able to integrate both the internal formation patterns and the surrounding contextual clues for NER under the framework of HMMs. As a result, the performance of the system can be improved without losing its efficiency in training and tagging. We have tested our system using different public corpora. The results show that lexicalized HMMs can substantially improve NER performance over standard HMMs. The results also indicate that character-based tagging (viz. the tagging based on pure single-character words) is comparable to and can even outperform the relevant known-word based tagging when a lexicalization technique is applied.
Starting Page	19
Ending Page	25
Page Count	7
File Format	PDF
ISSN	19310145 19310153
DOI	10.1145/1089815.1089819
Journal	ACM SIGKDD Explorations Newsletter (SKDD)
Volume Number	7
Issue Number	1
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2000-06-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Known word tagging Lexicalized hidden markov models Character tagging Chinese named entity recognition
Content Type	Text
Resource Type	Article

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