NDLI: Semi-supervised Chinese compound word extraction based on HMM

Content Provider	IEEE Xplore Digital Library
Author	Hui He Bo Chen Jun Guo
Copyright Year	2008
Description	Author affiliation: Sch. of Inf. Eng., Beijing Univ. of Posts & Telecommun., Beijing (Hui He; Bo Chen; Jun Guo)
Abstract	In natural languages, compound words play an important role and their automatically extraction is very helpful in information retrieval, information extraction and text classification. We introduce a semi-supervised Chinese compound extraction approach based on HMM using bootstrapping in this paper. First, we define a set of tags BEMI {beginning, end, middle, independence}, which means the position of words in compounds. Then we employ HMM to extract compounds automatically in BEMI tagging algorithm. We rank the Compounds extracted from corpus by their word frequency and length in descending order, and add top N compounds in seed compounds list. The algorithm learns more Chinese compounds from corpus by bootstrapping. Experimental results show that this approach get much higher performance than unsupervised one. Different from those extracted by traditional methods, these Chinese compounds contain category information, which can be used in text classification/clustering as features. Also, this approach can be applied in keyword recommendation system in advertisement for different kinds of advertisers because of its expansibility and versatility.
Starting Page	2077
Ending Page	2081
File Size	97487
Page Count	5
File Format	PDF
ISBN	9781424421138
DOI	10.1109/WCICA.2008.4593244
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Compounds Data mining Tagging Hidden Markov models Mathematical model Feature extraction Equations BEMI tagging algorithm Compound word extraction Bootstrapping
Content Type	Text
Resource Type	Article

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