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Comparable Entity Mining from Comparative Questions
July 2013 (vol. 25 no. 7)
pp. 1498-1509
Shasha Li, National University of Defense Technology, Beijing
Chin-Yew Lin, Microsoft Research Asia, Beijing
Young-In Song, Microsoft Research Asia, Beijing
Zhoujun Li, Beihang University, Beijing
Comparing one thing with another is a typical part of human decision making process. However, it is not always easy to know what to compare and what are the alternatives. In this paper, we present a novel way to automatically mine comparable entities from comparative questions that users posted online to address this difficulty. To ensure high precision and high recall, we develop a weakly supervised bootstrapping approach for comparative question identification and comparable entity extraction by leveraging a large collection of online question archive. The experimental results show our method achieves F1-measure of 82.5 percent in comparative question identification and 83.3 percent in comparable entity extraction. Both significantly outperform an existing state-of-the-art method. Additionally, our ranking results show highly relevance to user's comparison intents in web.
Index Terms:
Reliability,Portable media players,Data mining,Equations,Algorithm design and analysis,Cities and towns,Pattern matching,comparable entity mining,Information extraction,bootstrapping,sequential pattern mining
Shasha Li, Chin-Yew Lin, Young-In Song, Zhoujun Li, "Comparable Entity Mining from Comparative Questions," IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 7, pp. 1498-1509, July 2013, doi:10.1109/TKDE.2011.210
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