| With the ubiquity of the Internet and the rapid development of Web 2.0 technology, Question Answering (QA) websites have become extremely popular knowledge sharing platforms. As the number of posted questions and answers continues to increase rapidly, the massive amount of question-answer knowledge is causing information overload. The problem is compounded by the growing number of redundant QAs. QA websites, such as Yahoo! Answer, are open platforms where users can ask or answer questions freely. Users may also wish to learn more about the information provided in an answer, so they can use related keywords in the answer to search for extended complementary information. In this paper, we propose a novel approach to identify complementary QAs of a target QA. We define two types of complementation - partial complementation and extended complementation. We utilize a decision-tree classification approach to construct a classification model and predict complementary relationships between QAs based on three measures: question similarity, answer novelty, and answer correlation. The results of experiments conducted on a dataset collected from Yahoo! Answers Taiwan show that the proposed approach can identify complementary QAs effectively. |
Updated 07/09/2013