title: Click-words: learning to predict document keywords from a user perspective.
abstract: BACKGROUND
  Recognizing words that are key to a document is important for ranking relevant scientific documents. Traditionally, important words in a document are either nominated subjectively by authors and indexers or selected objectively by some statistical measures. As an alternative, we propose to use documents' words popularity in user queries to identify click-words, a set of prominent words from the users' perspective. Although they often overlap, click-words differ significantly from other document keywords.
  
  
  RESULTS
  We developed a machine learning approach to learn the unique characteristics of click-words. Each word was represented by a set of features that included different types of information, such as semantic type, part of speech tag, term frequency-inverse document frequency (TF-IDF) weight and location in the abstract. We identified the most important features and evaluated our model using 6 months of PubMed click-through logs. Our results suggest that, in addition to carrying high TF-IDF weight, click-words tend to be biomedical entities, to exist in article titles, and to occur repeatedly in article abstracts. Given the abstract and title of a document, we are able to accurately predict the words likely to appear in user queries that lead to document clicks.
authors.affiliation: National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.
authors.forename: Rezarta
authors.initials: R
authors.lastname: Islamaj Doğan
authors.forename: Zhiyong
authors.initials: Z
authors.lastname: Lu
mesh_terms: D000043:Abstracting and Indexing / Q000379:methods*; D000465:Algorithms; D001185:Artificial Intelligence; D016239:MEDLINE; D039781:PubMed; D012660:Semantics
references.citation: Acad Emerg Med. 1999 Apr;6(4):292-6
references.pmid: 10230980
references.citation: Bioinformatics. 2009 Aug 1;25(15):1944-51
references.pmid: 19497938
references.citation: Proc IEEE Comput Syst Bioinform Conf. 2004;:394-404
references.pmid: 16448032
references.citation: Bioinformatics. 1998;14(7):600-7
references.pmid: 9730925
references.citation: Bioinformatics. 2009 Nov 15;25(22):3031-2
references.pmid: 19654114
references.citation: Bioinformatics. 2004 Sep 22;20(14):2320-1
references.pmid: 15073016
references.citation: BMC Bioinformatics. 2008 Sep 25;9:402
references.pmid: 18817555
references.citation: Database (Oxford). 2009;2009:bap018
references.pmid: 20157491
references.citation: Proc AMIA Symp. 2001;:17-21
references.pmid: 11825149
references.citation: Stud Health Technol Inform. 2004;107(Pt 1):292-6
references.pmid: 15360821
references.citation: J Am Med Inform Assoc. 2009 Jan-Feb;16(1):32-6
references.pmid: 18952932
references.citation: Bioinformatics. 2008 Nov 1;24(21):2559-60
references.pmid: 18772154
references.citation: J Am Soc Inf Sci Technol. 2009 Feb;60(2):264-274
references.pmid: 29456459