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Knowl. Discov. Data"],"published-print":{"date-parts":[[2017,5,31]]},"abstract":"<jats:p>Interpreting the user intent in search queries is a key task in query understanding. Query intent classification has been widely studied. In this article, we go one step further to understand the query from the view of head--modifier analysis. For example, given the query \u201cpopular iphone 5 smart cover,\u201d instead of using coarse-grained semantic classes (e.g.,<jats:italic>find electronic product<\/jats:italic>), we interpret that \u201csmart cover\u201d is the head or the intent of the query and \u201ciphone 5\u201d is its modifier. Query head--modifier detection can help search engines to obtain particularly relevant content, which is also important for applications such as ads matching and query recommendation. We introduce an unsupervised semantic approach for query head--modifier detection. First, we mine a large number of instance level head--modifier pairs from search log. Then, we develop a conceptualization mechanism to generalize the instance level pairs to concept level. Finally, we derive weighted concept patterns that are concise, accurate, and have strong generalization power in head--modifier detection. The developed mechanism has been used in production for search relevance and ads matching. We use extensive experiment results to demonstrate the effectiveness of our approach.<\/jats:p>","DOI":"10.1145\/2988235","type":"journal-article","created":{"date-parts":[[2016,12,27]],"date-time":"2016-12-27T13:51:31Z","timestamp":1482846691000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Unsupervised Head--Modifier Detection in Search Queries"],"prefix":"10.1145","volume":"11","author":[{"given":"Zhongyuan","family":"Wang","sequence":"first","affiliation":[{"name":"Renmin University of China, Microsoft Research, Asia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fang","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Software Development Environment, Beihang University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haixun","family":"Wang","sequence":"additional","affiliation":[{"name":"Facebook Inc."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhirui","family":"Hu","sequence":"additional","affiliation":[{"name":"Harvard University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Yan","sequence":"additional","affiliation":[{"name":"Microsoft Research, Asia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fangtao","family":"Li","sequence":"additional","affiliation":[{"name":"Google Research"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ji-Rong","family":"Wen","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Big Data Management and Analysis Methods, Renmin University of China."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhoujun","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Software Development Environment, Beihang University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2016,12,26]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772692"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/336597.336644"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1718487.1718492"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1376616.1376746"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961199"},{"key":"e_1_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Jackie Chi Kit Cheung and Xiao Li. 2012a. Sequence clustering and labeling for unsupervised query intent discovery. In WSDM. ACM 383--392. Jackie Chi Kit Cheung and Xiao Li. 2012a. Sequence clustering and labeling for unsupervised query intent discovery. In WSDM. ACM 383--392.","DOI":"10.1145\/2124295.2124342"},{"key":"e_1_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Jackie Chi Kit Cheung and Xiao Li. 2012b. Sequence clustering and labeling for unsupervised query intent discovery. In WSDM. ACM 383--392. Jackie Chi Kit Cheung and Xiao Li. 2012b. Sequence clustering and labeling for unsupervised query intent discovery. In WSDM. 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ACM, 48--53. Hang Li, Gu Xu, Bruce Croft, and Michael Bendersky. 2011. Query representation and understanding. In SIGIR Workshop on Query Representation and Understanding, Vol. 44. ACM, 48--53."},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/2505515.2505567"},{"key":"e_1_2_1_17_1","unstructured":"Xiao Li. 2010. Understanding the semantic structure of noun phrase queries. In ACL. ACL 1337--1345. Xiao Li. 2010. Understanding the semantic structure of noun phrase queries. In ACL. ACL 1337--1345."},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/1390334.1390393"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/2505515.2505694"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/1117454.1117466"},{"key":"e_1_2_1_21_1","unstructured":"Marius Pa\u015fca and Benjamin Van Durme. 2008. Weakly-supervised acquisition of open-domain classes and class attributes from web documents and query logs. In ACL-08: HLT. 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