An unsupervised approach for low-quality answer detection in community question-answering

Abstract

Community Question Answering (CQA) sites such as Yahoo! Answers provide rich knowledge for people to access. However, the quality of answers posted to CQA sites often varies a lot from precise and useful ones to irrelevant and useless ones. Hence, automatic detection of low-quality answers will help the site managers efficiently organize the accumulated knowledge and provide high-quality contents to users. In this paper, we propose a novel unsupervised approach to detect lowquality answers at a CQA site. The key ideas in our model are: (1) most answers are normal; (2) low-quality answers can be found by checking its “peer” answers under the same question; (3) different questions have different answer quality criteria. Based on these ideas, we devise an unsupervised learning algorithm to assign soft labels to answers as quality scores. Experiments show that our model significantly outperforms the other state-of-the-art models on answer quality prediction.