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.
In the Q&A field, another corpus, The Question Paraphrase corpus REF , was built by collecting from WikiAnswers 7434 sentences formed by 1000 different questions and their paraphrases.
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