Final published version
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Publication date | 27/06/2018 |
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Host publication | SIGIR '18 The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval |
Place of Publication | New York |
Publisher | Association for Computing Machinery, Inc |
Pages | 1153-1156 |
Number of pages | 4 |
ISBN (electronic) | 9781450356572 |
<mark>Original language</mark> | English |
Event | 41st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018 - Ann Arbor, United States Duration: 8/07/2018 → 12/07/2018 |
Conference | 41st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018 |
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Country/Territory | United States |
City | Ann Arbor |
Period | 8/07/18 → 12/07/18 |
Conference | 41st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018 |
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Country/Territory | United States |
City | Ann Arbor |
Period | 8/07/18 → 12/07/18 |
Plenty of research attempts target the automatic duplicate detection in Community Question Answering (CQA) systems and frame the task as a supervised learning problem on the question pairs. However, these methods rely on handcrafted features, leading to the difficulty of distinguishing related and duplicate questions as they are often textually similar. To tackle this issue, we propose to leverage neural network architecture to extract "deep" features to identify whether a question pair is duplicate or related. In particular, we construct question correlation matrices, which capture the word-wise similarities between questions. The constructed matrices are input to our proposed convolutional neural network (CNN), in which the convolutional operation moves through the two dimensions of the matrices. Empirical studies on a range of real-world CQA datasets confirm the effectiveness of our proposed correlation matrices and the CNN. Our method outperforms the state-of-the-art methods and achieves better classification performance.