Abstract
Community-driven Question Answering (CQA) systems that crowdsource experiential information in the form of questions and answers and have accumulated valuable reusable knowledge. Clustering of QA datasets from CQA systems provides a means of organizing the content to ease tasks such as manual curation and tagging. In this paper, we present a clustering method that exploits the two-part question-answer structure in QA datasets to improve clustering quality. Our method, {\it MixKMeans}, composes question and answer space similarities in a way that the space on which the match is higher is allowed to dominate. This construction is motivated by our observation that semantic similarity between question-answer data (QAs) could get localized in either space. We empirically evaluate our method on a variety of real-world labeled datasets. Our results indicate that our method significantly outperforms state-of-the-art clustering methods for the task of clustering question-answer archives.
Original language | English |
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Title of host publication | Proceedings of the Conference on Empirical Methods in Natural Language Processing 2016 |
Publisher | Association for Computing Machinery |
Publication status | Published - 06 Nov 2016 |
Event | Conference on Empirical Methods in Natural Language Processing - Texas, Austin, United States Duration: 02 Nov 2016 → 06 Nov 2016 http://www.emnlp2016.net/ |
Conference
Conference | Conference on Empirical Methods in Natural Language Processing |
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Abbreviated title | EMNLP 2016 |
Country/Territory | United States |
City | Austin |
Period | 02/11/2016 → 06/11/2016 |
Internet address |