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Web"],"published-print":{"date-parts":[[2020,8,31]]},"abstract":"<jats:p>We propose a ranking framework, called PatternRank+NN, for expanding a set of seed entities of a particular class (i.e., entity set expansion) from Web search queries. PatternRank+NN consists of two parts: PatternRank and NN. Unlike the traditional methods, PatternRank brings user behaviors into entity set expansion from Web search queries. PatternRank is a Markov chain which simulates the Web search query process of users on the graph model for Web search query log, and ranks the features of the class. The features in the front rank are used to generate candidate entities of the class. NN, a ranking strategy called Nearest Neighbor, ranks these candidate entities such that the set of seed entities can be expanded from the candidate entities in the front rank. Our experiments demonstrate the superior performance of PatternRank+NN in comparison with the state-of-the-art methods.<\/jats:p>","DOI":"10.1145\/3386042","type":"journal-article","created":{"date-parts":[[2020,5,4]],"date-time":"2020-05-04T23:50:36Z","timestamp":1588636236000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["PatternRank+NN"],"prefix":"10.1145","volume":"14","author":[{"given":"Zhijun","family":"Xiao","sequence":"first","affiliation":[{"name":"Key Laboratory of Data Engineering and Knowledge Engineering, School of Information, Renmin University of China, Zhongguancun Street, Haidian District, Beijing, China"}]},{"given":"Cuiping","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Data Engineering and Knowledge Engineering, School of Information, Renmin University of China, Zhongguancun Street, Haidian District, Beijing, China"}]},{"given":"Hong","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Data Engineering and Knowledge Engineering, School of Information, Renmin University of China, Zhongguancun Street, Haidian District, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2020,5,3]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401995"},{"volume-title":"Proceedings of WSDM\u201916","author":"Chen Zhe","key":"e_1_2_1_2_1","unstructured":"Zhe Chen , Michael Cafarella , and H. 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Distributional similarity vs. PU learning for entity set expansion. In Proceedings of ACL\u201910. 359--364."},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.3115\/1699571.1699635"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/1321440.1321536"},{"key":"e_1_2_1_20_1","volume-title":"Proceedings of IJCAI\u201907","author":"Pasca Marius","year":"2007","unstructured":"Marius Pasca and Benjamin Van Durme . 2007 . What you seek is what you get: Extraction of class attributes from query logs . In Proceedings of IJCAI\u201907 . 2832--2837. Marius Pasca and Benjamin Van Durme. 2007. What you seek is what you get: Extraction of class attributes from query logs. In Proceedings of IJCAI\u201907. 2832--2837."},{"key":"e_1_2_1_21_1","doi-asserted-by":"crossref","unstructured":"G. Pass A. Chowdhury and C. Torgeson. 2006. A picture of search. In INFOSCALE (paper 1).  G. Pass A. Chowdhury and C. Torgeson. 2006. A picture of search. 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