Mining Alternative Actions from Community Q&A Corpus for Task-Oriented Web Search

Suppanut Pothirattanachaikul (Kyoto Univ.), Takehiro Yamamoto (Kyoto Univ.), Sumio Fujita, Akira Tajima, and Katsumi Tanaka (Kyoto Univ.)

ACM/IEEE WI 2017, 2017/8


Information Retrieval

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. We propose a method to compute how well two actions can be alternative actions by using a question-answer structure in a cQA corpus. Ourmethod builds a question-action bi-partite 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 our proposed method outperformed the query suggestion methods provided by the commercial search engines in terms of D#-nDCG.

Mining Alternative Actions from Community Q&A Corpus for Task-Oriented Web Search(External Site Link)