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Supplementary Q&A recommendation based on transfer learning enhanced multi-label multi-document classifier
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Li Ying,Li Ming
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(School of Economics and Management, China University of Petroleum-Beijing, Beijing 102249, China)
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Abstract
[Objective] This paper proposes a supplementary question and answer (Q&A) recommendation method based on the transfer learning enhanced multi-label multi-document Q&A classification model, aiming to identify and recommend supplementary Q&A in community question answering.
[Methods] We proposed new features and classified Q&A supplementary relationships combining existing features. Then, we built a transfer learning enhanced multi-label multi-document Q&A classification model to identify and recommend supplementary Q&As.
[Results] Experiments are conducted on three meta-tasks using real datasets. Results demonstrate that the proposed method improves 48.3%, 15.8%, and 32.5% in the precision, recall, and f-measure separately.
[Limitations] The method has only been applied to health Q&A topics on the Zhihu platform. The performance on different platforms with different topics of Q&A remains to be verified.
[Conclusions] The proposed recommendation method can effectively recommend supplementary Q&As. It helps users in community question answering acquire knowledge more comprehensively and promote knowledge utilization in the community.
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Published: 19 April 2024
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