TY - JOUR
T1 - Topical keyphrase extraction with hierarchical semantic networks
AU - Sung, Yoo yeon
AU - Kim, Seoung Bum
N1 - Funding Information:
The authors would like to thank the editor and reviewers for their useful comments and suggestions, which were greatly help in improving the quality of the paper. This research was supported by Brain Korea PLUS , the Ministry of Trade, Industry and Energy under Industrial Technology Innovation Program ( R1623371 ) and the Institute for Information & Communications Technology Promotion grant funded by the Korea government (No. 2018-0-00440 , ICT-based Crime Risk Prediction and Response Platform Development for Early Awareness of Risk Situation), and the Ministry of Culture, Sports and Tourism and Korea Creative Content Agency in the Culture Technology Research & Development Program 2019.
PY - 2020/1
Y1 - 2020/1
N2 - Topical keyphrase extraction is used to summarize large collections of text documents. However, traditional methods cannot properly reflect the intrinsic semantics and relationships of keyphrases because they rely on a simple term-frequency-based process. Consequently, these methods are not effective in obtaining significant contextual knowledge. To resolve this, we propose a topical keyphrase extraction method based on a hierarchical semantic network and multiple centrality network measures that together reflect the hierarchical semantics of keyphrases. We conduct experiments on real data to examine the practicality of the proposed method and to compare its performance with that of existing topical keyphrase extraction methods. The results confirm that the proposed method outperforms state-of-the-art topical keyphrase extraction methods in terms of the representativeness of the selected keyphrases for each topic. The proposed method can effectively reflect intrinsic keyphrase semantics and interrelationships.
AB - Topical keyphrase extraction is used to summarize large collections of text documents. However, traditional methods cannot properly reflect the intrinsic semantics and relationships of keyphrases because they rely on a simple term-frequency-based process. Consequently, these methods are not effective in obtaining significant contextual knowledge. To resolve this, we propose a topical keyphrase extraction method based on a hierarchical semantic network and multiple centrality network measures that together reflect the hierarchical semantics of keyphrases. We conduct experiments on real data to examine the practicality of the proposed method and to compare its performance with that of existing topical keyphrase extraction methods. The results confirm that the proposed method outperforms state-of-the-art topical keyphrase extraction methods in terms of the representativeness of the selected keyphrases for each topic. The proposed method can effectively reflect intrinsic keyphrase semantics and interrelationships.
KW - Hierarchical networks
KW - Phrase rankings
KW - Semantic relationships
KW - Text mining
KW - Topical keyphrase extraction
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U2 - 10.1016/j.dss.2019.113163
DO - 10.1016/j.dss.2019.113163
M3 - Article
AN - SCOPUS:85074529522
VL - 128
JO - Decision Support Systems
JF - Decision Support Systems
SN - 0167-9236
M1 - 113163
ER -