Abstract
Answer Extraction of Web-based Question Answering aims to extract answers from snippets retrieved by search engines. Search results contain lots of noisy and incomplete texts, thus the task becomes more challenging comparing with traditional answer extraction upon off-line corpus. In this paper we discuss the important role of employing multiple extraction engines for Web-based Question Answering. Aggregating multiple engines could ease the negative effect from the noisy search results on single method. We adopt a Pruned Rank Aggregation method which performs pruning while aggregating candidate lists provided by multiple engines. It fully leverages redundancies within and across each list for reducing noises in candidate list without hurting answer recall. In addition, we rank the aggregated list with a Learning to Rank framework with similarity, redundancy, quality and search features. Experiment results on TREC data show that our method is effective for reducing noises in candidate list, and greatly helps to improve answer ranking results. Our method outperforms state-of-the-art answer extraction method, and is sufficient in dealing with the noisy search snippets for Web-based QA.
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Sun, H., Wei, F., Zhou, M. (2014). Answer Extraction with Multiple Extraction Engines for Web-Based Question Answering. In: Zong, C., Nie, JY., Zhao, D., Feng, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2014. Communications in Computer and Information Science, vol 496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45924-9_29
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DOI: https://doi.org/10.1007/978-3-662-45924-9_29
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