Abstract
Almost every text search engine uses snippets to help users quickly assess the relevance of retrieved items in the ranked list. Although answer-contained snippets can help to improve the effectiveness of search intuitively, quantitative study of such intuition remains untouched. In this paper, we first propose a simple answer-contained snippet method for community-based Question and Answer (cQA) search, and then compare our method with the state-of-the-art traditional snippet algorithms. The experimental results show that the answer-contained snippet method significantly outperforms the state-of-the-art traditional methods, considering relevance judgements and information satisfaction evaluations.
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Notes
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In this paper, the user question has the same meaning as the user query.
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The data presented in Table 2 were acquired by averaging the results for each query over the total number of queries, thus producing the average recall, precision and \(F_1\) values per query.
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Acknowledgments
This work was supported by 863 Program (2015AA015404), China National Science Foundation (61402036, 60973083, 61273363), Beijing Technology Project (Z151100001615029), Science and Technology Planning Project of Guangdong Province (2014A010103009, 2015A020217002), Guangzhou Science and Technology Planning Project (201604020179).
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Mao, XL., Wang, D., Hao, YJ., Yuan, W., Huang, H. (2016). A Comparative Study of Answer-Contained Snippets and Traditional Snippets. In: Ma, S., et al. Information Retrieval Technology. AIRS 2016. Lecture Notes in Computer Science(), vol 9994. Springer, Cham. https://doi.org/10.1007/978-3-319-48051-0_5
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