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author:

Liao, X.-W. (Liao, X.-W..) [1] | Liu, D.-Y. (Liu, D.-Y..) [2] | Gui, L. (Gui, L..) [3] | Cheng, X.-Q. (Cheng, X.-Q..) [4] | Chen, G.-L. (Chen, G.-L..) [5]

Indexed by:

Scopus PKU CSCD

Abstract:

Opinion retrieval is a hot topic in the research of natural language processing. Most existing approaches in text opinion retrieval can not extract knowledge and concept from context. They also lack opinion generalization ability and overlook the semantic relations between words. This paper proposes an opinion retrieval method based on knowledge graph conceptualization and network embedding. First, conceptual knowledge graph is used to conceptualize the queries and texts into the correct conceptual space while the nodes in the knowledge graph are embedded into low dimensional vectors space by network embedding technology. Then, the similarity between queries and texts is calculated based on embedding vectors. According to the similarity score, the opinion scores of texts can be captured based on statistical machine learning methods. Finally, the concept space, knowledge representation space, and opinion mining result serve opinion retrieval models. The experiment shows that the retrieval model proposed in this paper can effectively improve the retrieval performance of multiple retrieval models. Compared with referenced method based on unified opinion, the proposed approach improves the MAP scores by 6.1% and 9.3%, respectively. Compared with referenced method based on learning to rank, proposed approach improves the MAP scores by 2.3% and 14.6%, respectively. © Copyright 2018, Institute of Software, the Chinese Academy of Sciences. All rights reserved.

Keyword:

Information retrieval; Knowledge graph; Network embedding; Opinion retrieval; Text conceptualization

Community:

  • [ 1 ] [Liao, X.-W.]College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350116, China
  • [ 2 ] [Liao, X.-W.]Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing (Fuzhou University), Fuzhou, 350116, China
  • [ 3 ] [Liu, D.-Y.]College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350116, China
  • [ 4 ] [Liu, D.-Y.]Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing (Fuzhou University), Fuzhou, 350116, China
  • [ 5 ] [Gui, L.]College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350116, China
  • [ 6 ] [Gui, L.]Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing (Fuzhou University), Fuzhou, 350116, China
  • [ 7 ] [Cheng, X.-Q.]Key Laboratory of Network Data Science and Technology (The Chinese Academy of Sciences), Beijing, 100190, China
  • [ 8 ] [Chen, G.-L.]College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350116, China
  • [ 9 ] [Chen, G.-L.]Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing (Fuzhou University), Fuzhou, 350116, China

Reprint 's Address:

  • [Gui, L.]College of Mathematics and Computer Science, Fuzhou UniversityChina

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Source :

Journal of Software

ISSN: 1000-9825

Year: 2018

Issue: 10

Volume: 29

Page: 2899-2914

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 4

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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