Publication Type

Conference Proceeding Article

Version

submittedVersion

Publication Date

1-2009

Abstract

Community Question Answering (QA) portals contain questions and answers contributed by hundreds of millions of users. These databases of questions and answers are of great value if they can be used directly to answer questions from any user. In this research, we address this collaborative QA task by drawing knowledge from the crowds in community QA portals such as Yahoo! Answers. Despite their popularity, it is well known that answers in community QA portals have unequal quality. We therefore propose a quality-aware framework to design methods that select answers from a community QA portal considering answer quality in addition to answer relevance. Besides using answer features for determining answer quality, we introduce several other quality-aware QA methods using answer quality derived from the expertise of answerers. Such expertise can be question independent or question dependent. We evaluate our proposed methods using a database of 95K questions and 537K answers obtained from Yahoo! Answers. Our experiments have shown that answer quality can improve QA performance significantly. Furthermore, question dependent expertise based methods are shown to outperform methods using answer features only. It is also found that there are also good answers not among the best answers identified by Yahoo! Answers users.

Keywords

Answer quality, Expertise, Question answering

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Publication

WSDM '09: Proceedings of the Second ACM International Conference on Web Search and Data Mining

First Page

142

Last Page

151

ISBN

9781605583907

Identifier

10.1145/1498759.1498820

Publisher

ACM

Additional URL

http://doi.org/10.1145/1498759.1498820

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