This paper proposes mining query subtopics from questions
\nin community question answering (CQA). The subtopics are
\nrepresented as a number of clusters of questions with keywords
\nsummarizing the clusters. The task is unique in that the
\nsubtopics from questions can not only facilitate user browsing
\nin CQA search, but also describe aspects of queries from
\na question-answering perspective. The challenges of the task
\ninclude how to group semantically similar questions and how
\nto find keywords capable of summarizing the clusters. We
\nformulate the subtopic mining task as a non-negative matrix
\nfactorization (NMF) problem and further extend the model of
\nNMF to incorporate question similarity estimated from metadata
\nof CQA into learning. Compared with existing methods,
\nour method can jointly optimize question clustering and keyword
\nextraction and encourage the former task to enhance the
\nlatter. Experimental results on large scale real world CQA
\ndatasets show that the proposed method significantly outperforms
\nthe existing methods in terms of keyword extraction,
\nwhile achieving a comparable performance to the state-of-the-art
\nmethods for question clustering.<\/p>\n","protected":false},"excerpt":{"rendered":"
This paper proposes mining query subtopics from questions in community question answering (CQA). The subtopics are represented as a number of clusters of questions with keywords summarizing the clusters. The task is unique in that the subtopics from questions can not only facilitate user browsing in CQA search, but also describe aspects of queries from 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