View Item 
      •   Home
      • 1. Schools
      • College of Engineering
      • School of Computer Science and Engineering (SCSE)
      • SCSE Conference Papers
      • View Item
      •   Home
      • 1. Schools
      • College of Engineering
      • School of Computer Science and Engineering (SCSE)
      • SCSE Conference Papers
      • View Item
      JavaScript is disabled for your browser. Some features of this site may not work without it.
      Subject Lookup

      Browse

      All of DR-NTUCommunities & CollectionsTitlesAuthorsBy DateSubjectsThis CollectionTitlesAuthorsBy DateSubjects

      My Account

      Login

      Statistics

      Most Popular ItemsStatistics by CountryMost Popular Authors

      About DR-NTU

      Predicting event-relatedness of popular queries

      Thumbnail
      cikm1295-ghoreishi.pdf (117.7Kb)
      Author
      Ghoreishi, Seyyedeh Newsha
      Sun, Aixin
      Date of Issue
      2013
      Conference Name
      International conference on Conference on information & knowledge management (22nd : 2013 : Burlingame, USA)
      School
      School of Computer Engineering
      Research Centre
      Centre for Advanced Information Systems
      Version
      Accepted version
      Abstract
      Many but not all popular queries are related to ongoing or recent events. In this paper, we identify 20 features including both contextual and temporal features from a small set of search results of a query and predict its event-relatedness. Search results from news and blog search engines are evaluated. Our analysis shows that the number of named entities in search results and their appearances in Wikipedia are among the most discriminating features for query event-relatedness prediction. Our study also shows that contextual features are more effective than temporal features. Evaluated with four classifiers (i.e., Support Vector Machine, Naive Bayes, Multinomial Logistic Regression, and Bayesian Logistic Regression) on two datasets, our experiments show that query event-relatedness can be predicted with high accuracy using the proposed features.
      Subject
      DRNTU::Engineering::Computer science and engineering::Information systems::Information systems applications
      Type
      Conference Paper
      Rights
      © 2013 ACM. This is the author created version of a work that has been peer reviewed and accepted for publication by Proceedings of the 22nd ACM international conference on Conference on information & knowledge management (CIKM '13), ACM. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [DOI:http://dx.doi.org/10.1145/2505515.2507853].
      Collections
      • SCSE Conference Papers
      http://dx.doi.org/10.1145/2505515.2507853
      Get published version (via Digital Object Identifier)

      Show full item record


      NTU Library, Nanyang Avenue, Singapore 639798 © 2011 Nanyang Technological University. All rights reserved.
      DSpace software copyright © 2002-2015  DuraSpace
      Contact Us | Send Feedback
      Share |    
      Theme by 
      Atmire NV
       

       


      NTU Library, Nanyang Avenue, Singapore 639798 © 2011 Nanyang Technological University. All rights reserved.
      DSpace software copyright © 2002-2015  DuraSpace
      Contact Us | Send Feedback
      Share |    
      Theme by 
      Atmire NV
       

       

      DCSIMG