Journal of Information Processing
Online ISSN : 1882-6652
ISSN-L : 1882-6652
Mining Alternative Actions from Community Q&A Corpus
Suppanut PothirattanachaikulTakehiro YamamotoSumio FujitaAkira TajimaKatsumi TanakaMasatoshi Yoshikawa
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2018 Volume 26 Pages 427-438


Web searchers often use a Web search engine to find a way or means to achieve his/her goal. For example, a user intending to solve his/her sleeping problem, the query “sleeping pills” may be used. However, there may be another solution to achieve the same goal, such as “have a cup of hot milk” or “stroll before bedtime.” The problem is that the user may not be aware that these solutions exist. Thus, he/she will probably choose to take a sleeping pill without considering these solutions. In this study, we define and tackle the alternative action mining problem. In particular, we attempt to develop a method for mining alternative actions for a given query. We define alternative actions as actions which share the same goal and define the alternative action mining problem as similar in the search result diversification. To tackle the problem, we propose leveraging a community Q&A (cQA) corpus for mining alternative actions. The cQA corpus can be seen as an archival dataset comprising dialogues between questioners, who want to know the solutions to their problem, and respondents, who suggest different solutions. We propose a method to compute how well two actions can be alternative actions by using a question-answer structure in a cQA corpus. Our method builds a question-action bipartite graph and recursively computes how well two actions can be alternative actions. We conducted experiments to investigate the effectiveness of our method using two newly built test collections, each containing 50 queries. The experimental results indicated that, for Japanese test collection, our proposed method significantly outperformed two types of baselines, one used the conventional query suggestions and the other extracted alternative-actions from the Web documents, in terms of D#-nDCG@8. Also, for English test collection, our method significantly outperformed the baseline using the conventional query suggestions in terms of D#-nDCG@8.

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© 2018 by the Information Processing Society of Japan
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