NDLI: Chinese Named Entity Recognition with new contextual features

Content Provider	IEEE Xplore Digital Library
Author	Ying Qin Taozheng Zhang Xiaojie Wang
Copyright Year	2008
Description	Author affiliation: Nat. Res. Center for Foreign Language Educ., Beijing Foreign Studies Univ., Beijing (Ying Qin) \|\| Beijing Univ. of Posts & Telecommun., Beijing (Taozheng Zhang; Xiaojie Wang)
Abstract	Chinese named entity recognition (NER) is studied in two directions: inner structure and outer surroundings. Inner structural analyses induce constitutions of person, location and organization name from the point of linguistics. However inner structural rules for named entities only provide necessary conditions for a sequence of Chinese characters being an entity name but not sufficient. Whether a string being a proper name or not is also determined by its contextual information or sometimes common sense. We build Chinese NER system based on supervised machine learning using features induced from simple inner structure and contextual information. We compare some NER approaches. The experimental results indicate complicated cases of various NER strategies. Then this paper turns to explore contextual features of named entities on large scale corpus, seeking for contextual evidence for different strategies of NER and mark words giving clues to the occurrence of NE. Finally, we apply some conclusions to improve NER system by enriching features in model and enhance the performance distinctly.
Starting Page	1
Ending Page	6
File Size	258925
Page Count	6
File Format	PDF
ISBN	9781424445158
DOI	10.1109/NLPKE.2008.4906794
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	conditional random fields contextual features Natural languages recognition model Predictive models Information retrieval Data mining Hidden Markov models Machine learning Named entity recognition Feature extraction Large-scale systems Context modeling Constitution
Content Type	Text
Resource Type	Article

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