An Automatic Question Answering Method for Small-Scale Corpus

Abstract

It is a growing trend for automatic question answering system to be prominent in the development process of society. There are many methods trying to address this problem, but with deficiencies—relatively developed methods based on template matching need a lot of manual work writing templates, and machine learning based methods need plenty of work collecting a large number of corpus, bring huge burden on small-scale scenery. Facing these problems, we propose an automatic question answering methods meeting the needs of small-scale corpus. This method consists of combining an improved text similarity calculation algorithm and an intention recognition method based on slot filling. We conduct experiments on the problem sets of related fields, and it shows a good performance of the proposed automatic question answering method. Our methods make small scene applications for dialog systems more practical.