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Title: A discriminative model approach for suggesting tags automatically for stack overflow questions
Authors: Saha, Avigit K.
Saha, Ripon K.
Schneider, Kevin A.
Issue Date: 2013
Publisher: IEEE
Abstract: Annotating 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.
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