tailieunhanh - Báo cáo khoa học: "Modeling Semantic Relevance for Question-Answer Pairs in Web Social Communities"

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. . | Modeling Semantic Relevance for Question-Answer Pairs in Web Social Communities Baoxun Wang Xiaolong Wang Chengjie Sun Bingquan Liu Lin Sun School of Computer Science and Technology Harbin Institute of Technology Harbin China bxwang wangxl cjsun liubq lsun @ 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 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. 1 Introduction In natural language processing NLP and information retrieval IR fields question answering QA problem has attracted much attention over the past few years. Nevertheless most of the QA researches mainly focus on locating the exact answer to a given factoid question in the related documents. The most well known international evaluation on the factoid QA task is the Text REtrieval Conference TREC 1 and the annotated questions and answers released by TREC have become important resources for the researchers. However when facing a non-factoid question such as why how or what about however almost no automatic QA systems work very well. The user-generated question-answer pairs are definitely of great importance to solve the nonfactoid questions. Obviously these natural QA pairs are usually created during people s communication via Internet social media among which we are interested in the community-driven 1http question-answering cQA sites and online forums. The cQA sites or systems provide platforms where users can either ask questions or deliver .