NDLI: Identification of Noun Phrase with Various Granularities

Content Provider	IEEE Xplore Digital Library
Author	Ying Qin Xiaojie Wang Yixin Zhong
Copyright Year	2007
Description	Author affiliation: Sch. of Inf. Eng., Beijing Univ. of Posts & Telecommun., Beijing (Ying Qin; Xiaojie Wang; Yixin Zhong)
Abstract	Since noun phrases are the most popular phrases in texts, noun phrase identification is one of vital subtasks of natural language processing. Generally Chinese noun phrases have hierarchical inner structures. This paper proposes an approach of defining various levels of granularity for noun phrases, catering for different application demands. Three levels of granularity noun phrases are proposed, that is, concept noun phrase, base noun phrase and entire noun phrase. The task of noun phrase identification is to label word sequences with phrase tags. All granularity noun phrase identifications are cast as classification problem under certain encoding schemes. The experimental dataset is acquired empirically from Chinese Penn Treebank 5.1. F, measure of concept noun phrase, base noun phrase and entire noun phrase identification reaches 92.12%, 84.13% and 85.32% respectively.
Starting Page	197
Ending Page	202
File Size	6368850
Page Count	6
File Format	PDF
ISBN	9781424416103
DOI	10.1109/NLPKE.2007.4368033
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-30
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Natural languages Morphology Tin Information retrieval Natural language processing Data mining Sun
Content Type	Text
Resource Type	Article

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