Modeling Semantic Relevance for Question-Answer Pairs in Web Social Communities

Baoxun Wang, Xiaolong Wang, Chengjie Sun, Bingquan Liu, Lin Sun

Abstract: Quantifying the semantic relevance between questions and their candidate answers is essential to answer detection in social media corpora. In this paper, a deep belief network is proposed to model the semantic relevance for question-answer pairs. Observing the textual similarity between the community-driven question-answering (cQA) dataset and the forum dataset, we present a novel learning strategy to promote the performance of our method on the social community datasets without hand-annotating work. The experimental results show that our method outperforms the traditional approaches on both the cQA and the forum corpora.