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

ACL ID P10-1125
Title Modeling Semantic Relevance for Question-Answer Pairs in Web Social Communities
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2010
Authors

Quantifying the semantic relevance be- tween questions and their candidate an- swers 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 strat- egy to promote the performance of our method on the social community datasets without hand-annotating work. The ex- perimental results show that our method outperforms the traditional approaches on both the cQA and the forum corpora.