AN EFFECTIVE QUESTION ANSWERING ENGINE BASED ON SEMANTIC THESAURUS

Abstract

Information retrieval has been proven effective for identifying specific passages within large volumes of data in response to a user query. However, users must click and browse numerous documents returned by keyword search to identify their desired word segments. The root problem is that keyword search is not an ideal method for users to express their real intentions for getting suitable documents. To overcome this problem, QA systems seek to process the question statement in natural language manner and find out the implicit intention of the user query. This study proposes a Chinese Question Answering system based on HowNet and Autotag to enable the system with semantic processing capability. The experiment collected 1000 Chinese News items from the ChinaTimes web site and presented an MRR (Mean Reciprocal Rank) value at 0.84.