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2015 | OriginalPaper | Chapter

Answer Quality Assessment in CQA Based on Similar Support Sets

Authors : Zongsheng Xie, Yuanping Nie, Songchang Jin, Shudong Li, Aiping Li

Published in: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data

Publisher: Springer International Publishing

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Abstract

Community question answering portal (CQA) has become one of the most important sources for people to seek information from the Internet. With great quantity of online users ready to help, askers are willing to post questions in CQA and are likely to obtain desirable answers. However, the answer quality in CQA varies widely, from helpful answers to abusive spam. Answer quality assessment is therefore of great significance. Most of the existing approaches evaluate answer quality based on the relevance between questions and answers. Due to the lexical gap between questions and answers, these approaches are not quite satisfactory. In this paper, a novel approach is proposed to rank the candidate answers, which utilizes the support sets to reduce the impact of lexical gap between questions and answers. Firstly, similar questions are retrieved and support sets are produced with their high quality answers. Based on the assumption that high quality answers of similar questions would also have intrinsic similarity, the quality of candidate answers are then evaluated through their distance from the support sets in both aspects of content and structure. Unlike most of the existing approaches, previous knowledge from similar question-answer pairs are used to bridged the straight lexical and semantic gaps between questions and answers. Experiments are implemented on approximately 2.15 million real-world question-answer pairs from Yahoo! Answers to verify the effectiveness of our approach. The results on metrics of MAP@K and MRR show that the proposed approach can rank the candidate answers precisely.

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previous chapter Predicting Implicit Discourse Relations with Purely Distributed Representations
next chapter Learning to Rank Answers for Definitional Question Answering
Footnotes
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Metadata
Title
Answer Quality Assessment in CQA Based on Similar Support Sets
Authors
Zongsheng Xie
Yuanping Nie
Songchang Jin
Shudong Li
Aiping Li
Copyright Year
2015
Book
Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data

Print ISBN: 978-3-319-25815-7

Electronic ISBN: 978-3-319-25816-4

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-25816-4_25

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