Mining Domain Terminologies Using Search Engine's Query Log

文献类型： 外文期刊

第一作者： Ni, Weijian

作者： Ni, Weijian;Liu, Tong;Zeng, Qingtian;Xie, Nengfu

作者机构：

关键词： Domain terminology; search engine; query log; network embedding; transductive learning

期刊名称：ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING （ 影响因子：1.413； 五年影响因子：1.382 ）

ISSN： 2375-4699

年卷期： 2021 年 20 卷 6 期

页码：

收录情况： SCI

摘要： Domain terminologies are a basic resource for various natural language processing tasks. To automatically discover terminologies for a domain of interest, most traditional approaches mostly rely on a domain-specific corpus given in advance; thus, the performance of traditional approaches can only be guaranteed when collecting a high-quality domain-specific corpus, which requires extensive human involvement and domain expertise. In this article, we propose a novel approach that is capable of automatically mining domain terminologies using search engine's query log-a type of domain-independent corpus of higher availability, coverage, and timeliness than a manually collected domain-specific corpus. In particular, we represent query log as a heterogeneous network and formulate the task of mining domain terminology as transductive learning on the heterogeneous network. In the proposed approach, the manifold structure of domain-specificity inherent in query log is captured by using a novel network embedding algorithm and further exploited to reduce the need for the manual annotation efforts for domain terminology classification. We select Agriculture and Healthcare as the target domains and experiment using a real query log from a commercial search engine. Experimental results show that the proposed approach outperforms several state-of-the-art approaches.

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