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Home > Archive>Volume 29, Issue 10, 2018 >2899-2914. DOI:10.13328/j.cnki.jos.005548
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Opinion Retrieval Method Combining Text Conceptualization and Network Embedding
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National Natural Science Foundation of China (61772135, U1605251); Open Project of Key Laboratory of Network Data Science & Technology of the Chinese Academy of Sciences (CASNDST201708, CASNDST201606); Director's Project Fund of Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education (2017KF01); Natural Science Foundation of Fujian Province of China (2017J01755); CERNET Innovation Project (NGⅡ20160501)

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    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.

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廖祥文,刘德元,桂林,程学旗,陈国龙.融合文本概念化与网络表示的观点检索.软件学报,2018,29(10):2899-2914

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History
  • Received:July 20,2017
  • Revised:November 08,2017
  • Online: February 08,2018
Links:Institute of Software, CASChina Computer FederationCASNSFC
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