Target Detection and Knowledge Learning for Domain Restricted Question Answering

Mengdi Zhang, Tao Huang, Yixin Cao, Lei Hou

Abstract: Frequent Asked Questions(FAQ) answering in restricted domain has attracted increasing attentions in various areas. FAQ is a task to automated response user’s typical questions within specific domain. Most researches use NLP parser to analyze user’s intention and employ ontology to enrich the domain knowledge. However, syntax analysis performs poorly on the short and informal FAQ questions, and external ontology knowledge bases in specific domains are usually unavailable and expensive to manually construct. In our research, we propose a semi-automatic domain-restricted FAQ answering framework SDFA, without relying on any external resources. SDFA detects the targets of questions to assist both the fast domain knowledge learning and the answer retrieval. The proposed framework has been successfully applied in real project on bank domain. Extensive experiments on two large datasets demonstrate the effectiveness and efficiency of the approaches.