Improved automatic keyword extraction given more semantic knowledge
Refereed conference paper presented and published in conference proceedings

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AbstractGraph-based ranking algorithm such as TextRank shows a remarkable effect on keyword extraction. However, these algorithms build graphs only considering the lexical sequence of the documents. Hence, graphs generated by these algorithm can not reflect the semantic relationships between documents. In this paper, we demonstrate that there exists an information loss in the graph-building process from textual documents to graphs. These loss will lead to the misjudgment of the algorithm. In order to solve this problem, we propose a new approach called Topic-based TextRank. Different from the traditional algorithm, our approach takes the lexical meaning of the text unit (i.e. words and phrase) into account. The result of our experiments shows that our proposed algorithm can outperform the state-of-the-art algorithms.
All Author(s) ListYang K., Chen Z., Cai Y., Huang D.P., Leung H.-F.
Name of ConferenceInternational Workshop on Database Systems for Advanced Applications, DASFAA 2016
Start Date of Conference16/04/2016
End Date of Conference19/04/2016
Place of ConferenceDallas
Country/Region of ConferenceUnited States of America
Volume Number9645
PublisherSpringer Verlag
Place of PublicationGermany
Pages112 - 125
LanguagesEnglish-United Kingdom
KeywordsExtraction, Graph-based ranking algorithm, Semantic analysis, Topic model

Last updated on 2020-10-08 at 02:12