A discriminative model approach for suggesting tags automatically for stack overflow questions

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dc.contributor.authorSaha, Avigit K.
dc.contributor.authorSaha, Ripon K.
dc.contributor.authorSchneider, Kevin A.
dc.date.accessioned2015-07-29T19:13:36Z
dc.date.available2015-07-29T19:13:36Z
dc.date.issued2013
dc.description.abstractAnnotating documents with keywords or ‘tags’ is useful for categorizing documents and helping users find a document efficiently and quickly. Question and answer (Q&A) sites also use tags to categorize questions to help ensure that their users are aware of questions related to their areas of expertise or interest. However, someone asking a question may not necessarily know the best way to categorize or tag the question, and automatically tagging or categorizing a question is a challenging task. Since a Q&A site may host millions of questions with tags and other data, this information can be used as a training and test dataset for approaches that automatically suggest tags for new questions. In this paper, we mine data from millions of questions from the Q&A site Stack Overflow, and using a discriminative model approach, we automatically suggest question tags to help a questioner choose appropriate tags for eliciting a response.en_US
dc.description.refereedYesen_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/35549
dc.identifier.urihttp://hdl.handle.net/1880/50689
dc.publisherIEEEen_US
dc.publisher.urlhttp://dl.acm.org/citation.cfm?id=2487103en_US
dc.titleA discriminative model approach for suggesting tags automatically for stack overflow questionsen_US
dc.typeunknown
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