Description

In community-based question answering (CQA) services where answers are generated by human, users may expect better answers than an automatic question answering system. However, in some cases, the user generated answers provided by CQA archives are not always of high quality. Most existing works on answer quality prediction use the same model for all answers, despite the fact that each answer is intrinsically different. However, modeling each individual QA pair differently is not feasible in practice. To balance between efficiency and accuracy, we propose a hybrid hierarchy-of-classifiers framework to model the QA pairs. First, we analyze the question type to guide the selection of the right answer quality model. Second, we use the information from question analysis to predict the expected answer features and train the type-based quality classifiers to hierarchically aggregate an overall answer quality score. A ranking that combines both relevance and quality is required to make such archives usable for factual information retrieval. This task is challenging, as the structure and the contents of community QA archives for from the web setting. To address this problem we present a general ranking framework for factual information retrieval from social media. Results of a large scale evaluation demonstrate that our method is highly learning at retrieving well-formed, factual answers to questions, as evaluated on a standard factoid QA Team. We also propose a number of novel features that are effective in distinguishing the quality of answers. We tested the framework on a dataset of about 50 thousand QA pairs from Yahoo! Answer. The results show that our proposed framework is effective in identifying high quality answers. Moreover, further analysis reveals the ability of our framework to classify low quality answers more accurately than a single classifier approach. Online forums contain a huge amount of valuable user generated content. In this paper we address the problem of extracting question-answer pairs from forums. Question-answer pairs extracted from forums can be used to help Question Answering services among other applications.