Abstract
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.
| Original language | English |
|---|---|
| Article number | 113163 |
| Journal | Decision Support Systems |
| Volume | 128 |
| DOIs | |
| Publication status | Published - 2020 Jan |
Keywords
- Hierarchical networks
- Phrase rankings
- Semantic relationships
- Text mining
- Topical keyphrase extraction
ASJC Scopus subject areas
- Management Information Systems
- Information Systems
- Developmental and Educational Psychology
- Arts and Humanities (miscellaneous)
- Information Systems and Management
Cite this
Topical keyphrase extraction with hierarchical semantic networks. / Sung, Yoo yeon; Kim, Seoung Bum.
In: Decision Support Systems, Vol. 128, 113163, 01.2020.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Topical keyphrase extraction with hierarchical semantic networks
AU - Sung, Yoo yeon
AU - Kim, Seoung Bum
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
UR - http://www.scopus.com/inward/record.url?scp=85074529522&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85074529522&partnerID=8YFLogxK
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 -