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Automatically suggesting topics for augmenting text documents
Abstract We present a method for automated topic suWe present a method for automated topic suggestion. Given a plain-text input document, our algorithm produces a ranking of novel topics that could enrich the input document in a meaningful way. It can thus be used to assist human authors, who often fail to identify important topics relevant to the context of the documents they are writing. Our approach marries two algorithms originally designed for linking documents to Wikipedia articles, proposed by Milne and Witten [15] and West et al. [22], While neither of them can suggest novel topics by itself, their combination does have this capability. The key step towards finding missing topics consists in generalizing from a large background corpus using principal component analysis. In a quantitative evaluation we conclude that our method achieves the precision of human editors when input documents are Wikipedia articles, and we complement this result with a qualitative analysis showing that the approach also works well on other types of input documents.ks well on other types of input documents.
Abstractsub We present a method for automated topic suWe present a method for automated topic suggestion. Given a plain-text input document, our algorithm produces a ranking of novel topics that could enrich the input document in a meaningful way. It can thus be used to assist human authors, who often fail to identify important topics relevant to the context of the documents they are writing. Our approach marries two algorithms originally designed for linking documents to Wikipedia articles, proposed by Milne and Witten [15] and West et al. [22], While neither of them can suggest novel topics by itself, their combination does have this capability. The key step towards finding missing topics consists in generalizing from a large background corpus using principal component analysis. In a quantitative evaluation we conclude that our method achieves the precision of human editors when input documents are Wikipedia articles, and we complement this result with a qualitative analysis showing that the approach also works well on other types of input documents.ks well on other types of input documents.
Bibtextype inproceedings  +
Doi 10.1145/1871437.1871556  +
Has author Robert West + , Doina Precup + , Joelle Pineau +
Has extra keyword Eigenarticles + , Principal Components + , Qualitative analysis + , Quantitative evaluation + , Text document + , Text input + , Topic suggestion + , Wikipedia + , Algorithms + , Data mining + , Knowledge management + , Quality control + , Principal component analysis +
Has keyword Data mining + , Eigenarticles + , Principal component analysis + , Topic suggestion + , Wikipedia +
Isbn 9781450300995  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 929–938  +
Published in International Conference on Information and Knowledge Management, Proceedings +
Title Automatically suggesting topics for augmenting text documents +
Type conference paper  +
Year 2010 +
Creation dateThis property is a special property in this wiki. 6 November 2014 20:25:41  +
Categories Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Conference papers  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 6 November 2014 20:25:41  +
DateThis property is a special property in this wiki. 2010  +
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