Information overload is a well-known problem which can be particularly detrimental to learners. In this paper, we propose a method to support learners in the information seeking process which consists in answering their questions by retrieving question paraphrases and their corresponding answers from social Q&A sites. Given the novelty of this kind of data, it is crucial to get a better understanding of how questions in social Q&A sites can be automatically analysed and retrieved. We discuss and evaluate several pre-processing strategies and question similarity metrics, using a new question paraphrase corpus collected from the WikiAnswers Q&A site. The results show that viable performance levels of more than 80% accuracy can be obtained for the task of question paraphrase retrieval.
Bernhard et al. REF developed QP (The Question Paraphrase Corpus) with the purpose of better understanding of how questions in social Q&A sites can be automatically analyzed and retrieved.
Answering Learners’ Questions by Retrieving Question Paraphrases from Social Q&A Sites
Proceedings of the Third Workshop on Innovative Use of NLP for Building Educational Applications
Computer Science