基于邻居网络的科学文献关键词提取

doi:10.16451/j.cnki.issn1003-6059.201808007

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Abstract

Extracting keywords directly from a single document cannot satisfy the precision requirements of keyphrase extraction, and the existing methods for keyphrase extraction based on neighbor information are time-consuming. In this paper, common author relations in research papers are utilized to build a neighbor network, and neighbor network information as well as document content is used to extract keyphrases. Based on those, high frequency pairs of phrase co-occurrence in domain are incorporated to further acquire high-quality keyphrases. Experimental results demonstrate that the proposed method is more computationally efficient than the existing methods.

Key words： Common-Author Network Neighborhood Network Keyphrase Extraction Natural Language Processing Text Processing

Received: 19 March 2018

ZTFLH:

TP 391

Fund:

Supported by National Key Research and Development Program of China(No.2016YFB1000901), National Natural Science Foundation of China(No.61503114), Key Project of Education Department of Anhui Province(No.KJ2017A418).

Corresponding Authors: HUANG Xiaoling, Ph.D. candidate, lecturer. Her research interests include natural language processing and data mining.

About author:: WANG Hao, Ph.D., professor. His research interests include intelligent computing theory and software, distributed intelligence system and complex systems modeling. LI Lei, Ph.D., associate professor. His research interests include data mining, social computing and graph computing. FU Minglan, Ph.D. candidate. Her research interests include intelligent computing and artificial intelligence.

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	HUANG Xiaoling
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	FU Minglan

Cite this article:

HUANG Xiaoling,WANG Hao,LI Lei等. Keyphrase Extraction from Research Papers Using Neighborhood Networks[J]. , 2018, 31(8): 750-762.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201808007 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2018/V31/I8/750

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