期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2010
卷号：2010
出版社：ACL Anthology
摘要：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 questionanswering
(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.
