A discriminative model approach for suggesting tags automatically for stack overflow questions
MSR(2013)
摘要
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.
更多查看译文
关键词
site stack overflow,challenging task,appropriate tag,annotating document,overflow question,new question,categorizing document,test dataset,discriminative model approach,question tag,discriminative model,accuracy,prediction algorithms,support vector machines,predictive models,data mining,vectors,machine learning
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络