Keyphrase Extraction Using Knowledge Graphs
Refereed conference paper presented and published in conference proceedings

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AbstractExtracting keyphrases from documents automatically is an important and interesting task since keyphrases provide a quick summarization for documents. Although lots of efforts have been made on keyphrase extraction, most of the existing methods (the co-occurrence based methods and the statistic-based methods) do not take semantics into full consideration. The co-occurrence based methods heavily depend on the co-occurrence relations between two words in the input document, which may ignore many semantic relations. The statistic-based methods exploit the external text corpus to enrich the document, which introduces more unrelated relations inevitably. In this paper, we propose a novel approach to extract keyphrases using knowledge graphs, based on which we could detect the latent relations of two keyterms (i.e., noun words and named entities) without introducing many noises. Extensive experiments over real data show that our method outperforms the state-of-art methods including the graph-based co-occurrence methods and statistic-based clustering methods.
All Author(s) ListWei SHI, Weiguo ZHENG, Jeffrey Xu YU, Hong CHENG, Lei ZOU
Name of ConferenceAsia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint Conference on Web and Big Data (APWeb-WAIM 2017)
Start Date of Conference07/07/2017
End Date of Conference09/07/2017
Place of ConferenceBeijing
Country/Region of ConferenceChina
Proceedings TitleAPWeb-WAIM: Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint Conference on Web and Big Data
Series TitleLecture Notes in Computer Science
Number in Series0302-9743
Volume Number10366
LanguagesEnglish-United States

Last updated on 2020-20-10 at 01:11