{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T12:16:34Z","timestamp":1763727394186,"version":"3.41.2"},"reference-count":47,"publisher":"Emerald","issue":"5","license":[{"start":{"date-parts":[[2018,11,5]],"date-time":"2018-11-05T00:00:00Z","timestamp":1541376000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["EL"],"published-print":{"date-parts":[[2018,11,5]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>The purpose of this paper is to predict news intent by exploring contextual and temporal features directly mined from a general search engine query log.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>First, a ground-truth data set with correctly marked news and non-news queries was built. Second, a detailed analysis of the search goals and topics distribution of news\/non-news queries was conducted. Third, three news features, that is, the relationship between entity and contextual words extended from query sessions, topical similarity among clicked results and temporal burst point were obtained. Finally, to understand the utilities of the new features and prior features, extensive prediction experiments on SogouQ (a Chinese search engine query log) were conducted.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>News intent can be predicted with high accuracy by using the proposed contextual and temporal features, and the macro average F1 of classification is around 0.8677. Contextual features are more effective than temporal features. All the three new features are useful and significant in improving the accuracy of news intent prediction.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>This paper provides a new and different perspective in recognizing queries with news intent without use of such large corpora as social media (e.g. Wikipedia, Twitter and blogs) and news data sets. The research will be helpful for general-purpose search engines to address search intents for news events. In addition, the authors believe that the approaches described here in this paper are general enough to apply to other verticals with dynamic content and interest, such as blog or financial data.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/el-06-2017-0134","type":"journal-article","created":{"date-parts":[[2018,11,5]],"date-time":"2018-11-05T05:47:41Z","timestamp":1541396861000},"page":"938-958","source":"Crossref","is-referenced-by-count":4,"title":["Automatic prediction of news intent for search queries"],"prefix":"10.1108","volume":"36","author":[{"given":"Xiaojuan","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuguang","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","published-online":{"date-parts":[[2018,11,5]]},"reference":[{"first-page":"37","article-title":"On-line new event detection and tracking","year":"1998","key":"key2021041413230436200_ref001"},{"first-page":"315","article-title":"Sources of evidence for vertical selection","year":"2009","key":"key2021041413230436200_ref002"},{"first-page":"98","article-title":"The intention behind web queries","year":"2006","key":"key2021041413230436200_ref003"},{"first-page":"35","article-title":"Topic-specific analysis of search queries","year":"2009","key":"key2021041413230436200_ref004"},{"first-page":"330","article-title":"A system for new event detection","year":"2003","key":"key2021041413230436200_ref005"},{"first-page":"1","article-title":"Survey and evaluation of query intent detection methods","year":"2009","key":"key2021041413230436200_ref006"},{"issue":"2","key":"key2021041413230436200_ref007","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1145\/792550.792552","article-title":"Taxonomy of web search","volume":"36","year":"2002","journal-title":"ACM SIGIR Forum"},{"first-page":"1599","article-title":"Time-sensitive personalized query auto completion","year":"2014","key":"key2021041413230436200_ref008"},{"first-page":"123","article-title":"Deck: detecting events from web click-through data","year":"2008","key":"key2021041413230436200_ref009"},{"issue":"6","key":"key2021041413230436200_ref010","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/4236.968829","article-title":"Inferring user interest","volume":"5","year":"2001","journal-title":"IEEE Internet Computing"},{"issue":"1","key":"key2021041413230436200_ref011","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A coefficient of agreement for nominal scales","volume":"20","year":"1960","journal-title":"Educational and Psychological Measurement"},{"first-page":"325","article-title":"Probabilistic query expansion using query logs","year":"2002","key":"key2021041413230436200_ref012"},{"first-page":"182","article-title":"Integration of news content into web results","year":"2009","key":"key2021041413230436200_ref013"},{"first-page":"105","article-title":"On-line event detection from web news stream","year":"2010","key":"key2021041413230436200_ref014"},{"first-page":"1193","article-title":"Predicting event-relatedness of popular queries","year":"2013","key":"key2021041413230436200_ref015"},{"first-page":"282","article-title":"Web queries: the tip of the iceberg of the user\u2019s intent","year":"2011","key":"key2021041413230436200_ref016"},{"first-page":"417","article-title":"Detecting hot events from web search logs","year":"2010","key":"key2021041413230436200_ref017"},{"year":"2016","key":"key2021041413230436200_ref018","article-title":"WHUIR at the NTCIR-12 temporal intent disambiguation task"},{"first-page":"33","article-title":"A case study of using geographic cues to predict query news intent","year":"2009","key":"key2021041413230436200_ref019"},{"key":"key2021041413230436200_ref020","unstructured":"He, D.Q. and Goker, A. (2000), \u201cDetecting session boundaries from web user logs\u201d, available at: www.sis.pitt.edu\/\u223cdaqing\/docs\/he00detecting.pdf (accessed 10 May 2017)."},{"issue":"5","key":"key2021041413230436200_ref021","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1002\/asi.22814","article-title":"Session analysis of people search within a professional social network","volume":"64","year":"2013","journal-title":"Journal of the American Society for Information Science and Technology"},{"first-page":"217","article-title":"Overview of NTCIR-12 temporal information access (temporalia-2) task","year":"2016","key":"key2021041413230436200_ref022"},{"first-page":"347","article-title":"Click-through prediction for news queries","year":"2009","key":"key2021041413230436200_ref023"},{"first-page":"167","article-title":"Understanding temporal query dynamic","year":"2011","key":"key2021041413230436200_ref024"},{"first-page":"121","article-title":"Using names and topics for new event detection","year":"2005","key":"key2021041413230436200_ref025"},{"first-page":"297","article-title":"Text classification and named entities for new event detection","year":"2004","key":"key2021041413230436200_ref026"},{"issue":"1","key":"key2021041413230436200_ref027","doi-asserted-by":"crossref","first-page":"159","DOI":"10.2307\/2529310","article-title":"The measurement of observer agreement for categorical data","volume":"33","year":"1977","journal-title":"Biometrics"},{"first-page":"267","article-title":"Using time-Series for temporal intent disambiguation in NTCIR-12 temporalia","year":"2016","key":"key2021041413230436200_ref028"},{"key":"key2021041413230436200_ref029","first-page":"593","article-title":"Automatic query type identification based on click through information","volume-title":"Lecture Notes in Computer Science","year":"2006"},{"first-page":"62","article-title":"Use of query similarity for improving presentation of news verticals","year":"2011","key":"key2021041413230436200_ref030"},{"first-page":"682","article-title":"Coupling feature selection and machine learning methods for navigational query identification","year":"2006","key":"key2021041413230436200_ref031"},{"first-page":"31","article-title":"Crowdsourcing a news query classification dataset","year":"2010","key":"key2021041413230436200_ref032"},{"first-page":"253","article-title":"News vertical search: when and what to display to users","year":"2013","key":"key2021041413230436200_ref033"},{"issue":"6","key":"key2021041413230436200_ref034","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1177\/0165551515607277","article-title":"When time meets information retrieval: past proposals, current plans and future trends","volume":"42","year":"2016","journal-title":"Journal of Information Science"},{"first-page":"972","article-title":"Scalable and near real-time burst detection from e-commerce queries","year":"2008","key":"key2021041413230436200_ref035"},{"first-page":"13","article-title":"Understanding user goals in web search","year":"2011","key":"key2021041413230436200_ref036"},{"first-page":"16","article-title":"Exploratory analysis on heterogeneous tag-point patterns for ranking and extracting hot-spot related tags","year":"2012","key":"key2021041413230436200_ref037"},{"first-page":"288","article-title":"Kyoto at the NTCIR-12 temporalia task: Machine learning approach for temporal intent disambiguation subtask","year":"2016","key":"key2021041413230436200_ref038"},{"issue":"5","key":"key2021041413230436200_ref039","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TSMCA.2011.2157129","article-title":"Query-guided event detection from news and blog streams","volume":"41","year":"2011","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics \u2013 Part A: Systems and Humans"},{"issue":"2013","key":"key2021041413230436200_ref040","first-page":"1","article-title":"Event identification in web social media through named entity recognition and topic modeling","volume":"88","year":"2013","journal-title":"Journal of Data& Knowledge Engineering"},{"first-page":"131","article-title":"Identifying similarities, periodicities and bursts for online search queries","year":"2004","key":"key2021041413230436200_ref041"},{"issue":"3","key":"key2021041413230436200_ref042","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1002\/asi.22995","article-title":"Exploiting temporal characteristics of features for effectively discovering event episodes from news corpora","volume":"65","year":"2014","journal-title":"Journal of the Association for Information Science and Technology"},{"issue":"1\/2","key":"key2021041413230436200_ref043","first-page":"024","article-title":"Query intent detection based on query log mining","volume":"13","year":"2014","journal-title":"Journal of Web Engineering"},{"first-page":"1129","article-title":"Learning recurrent event queries for web search","year":"2010","key":"key2021041413230436200_ref044"},{"first-page":"484","article-title":"Event detection from evolution of click-through data","year":"2006","key":"key2021041413230436200_ref045"},{"first-page":"1339","article-title":"Learning to detect event-related queries for web search","year":"2015","key":"key2021041413230436200_ref046"},{"issue":"5","key":"key2021041413230436200_ref047","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1016\/0306-4573(88)90021-0","article-title":"Term-weighting approaches in automatic text retrieval","volume":"24","year":"1988","journal-title":"Information Processing and Management"}],"container-title":["The Electronic Library"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/EL-06-2017-0134\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/EL-06-2017-0134\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,25]],"date-time":"2025-07-25T01:07:25Z","timestamp":1753405645000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/el\/article\/36\/5\/938-958\/76649"}},"subtitle":["An exploration of contextual and temporal features"],"short-title":[],"issued":{"date-parts":[[2018,11,5]]},"references-count":47,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2018,11,5]]},"published-print":{"date-parts":[[2018,11,5]]}},"alternative-id":["10.1108\/EL-06-2017-0134"],"URL":"https:\/\/doi.org\/10.1108\/el-06-2017-0134","relation":{},"ISSN":["0264-0473"],"issn-type":[{"type":"print","value":"0264-0473"}],"subject":[],"published":{"date-parts":[[2018,11,5]]}}}