Exploiting Semantic Roles for Asynchronous Question Answering in an Educational Setting
Recent question answering (QA) research has started to incorporate deep natural language processing (NLP) such as syntactic and semantic parsing in order to enhance the capability of selecting the most relevant answers to a given question. However, current NLP technology involves intensive computing and thus hard to meet the real-time demand of synchronous QA. To improve e-learning we introduce NLP into a QA system that specifically exploits the communication latency between student and instructor. We present how the system will fit for educational environment, and how semantic similarity matching between a question and its candidate answers can be improved by semantic roles. The designed system and its running results show the perspective and potential of this research.
Keywordsquestion answering asynchronous QA semantic role labeling natural language processing e-learning
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