Question Answering (CQA) websites have become valu- able repositories which host a massive volume of human knowl- edge. To maximize the utility of such knowledge, it is essential to evaluate the quality of an existing question or answer, especially soon after it is posted on the CQA website. In this paper, we study the problem of inferring the quality of ques- tions and answers through a case study of a software CQA (Stack Overflow). Our key finding is that the quality of an answer is strongly positively correlated with that of its question. A rmed with this observation, we propose a family of algorithms to jointly pre- dict the quality of questions and answers, for both quantifying nu- merical quality scores and differentiating the high-quali ty ques- tions answers from those of low quality. We conduct extensive experimental evaluations to demonstrate the effectiveness and ef- ficiency of our methods.
Similar to question quality prediction, answer quality prediction could also be used to directly identify high-quality answers for a given user query. For example, @cite_17 and @cite_11 use human annotators to label the quality of the answers, and evaluate the usefulness of answer quality by incorporating it to improve retrieval performance.
@cite_11 @cite_17
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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. New types of document collections are being developed by various web services. The service providers keep track of non-textual features such as click counts. In this paper, we present a framework to use non-textual features to predict the quality of documents. We also show our quality measure can be successfully incorporated into the language modeling-based retrieval model. We test our approach on a collection of question and answer pairs gathered from a community based question answering service where people ask and answer questions. Experimental results using our quality measure show a significant improvement over our baseline.