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| Most community question answering (CQA) websites manage plenty of question answer pairs (QAPs) through topic-based organization, which cannot satisfy users' search demands. Facets of topics serve as a powerful tool for navigating, refining, and grouping the QAPs.In this work, we propose FACM, a model for facet annotation by extending Convolution Neural Network (CNN) with a matching strategy. First, considering the importance of topic phrases for QAPs in knowledge domain, phrase information is incorporated into text representation by a CNN with different kernel sizes. Then, through a matching strategy among QAPs and fact label texts (FaLTs) acquired from external knowledge base, we generate similarity matrices to deal with facet heterogeneity. Finally, a three-channel CNN is trained for facet label assignment of QAPs as a binary classifier.Experiments on three real-world datasets show that FACM outperforms three state-of-the-art methods. |
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| Keywords:Knowledge domain, Natural Language Processing, Facet Annotation, Matching Strategy, Convolutional Neural Network |
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