Research And Implementation Of Web-based Intelligent Question Answering System

Question Answering System is a very hot spot and difficulty spot in the research community of natural language processing, it combines natural language processing techniquese and information retrieval techniques etc. A Question Answering System can return user a concise and accurate answer for question in natural language. But there is still no mature Question Answering System exploited by now, because we know that let a computer to understand human language is so difficult. In this thesis, we have studied a Web-based Intelligent Question Answering System, which is a kind of Question Answering System based in distance education. If users submit a question when he is learning by network, this system can answer it immediately. By this way, the system can enhance the quality of distance education. The system studied by this thesis is based B/S architectures, it includes three models: question's semantic comprehension model, FAQ-based question similarity match model, document warehouse-base automatic answer fetching model. The question's semantic comprehension model combines many natural language processing techniques, including Segmentation and Part-Of-Speech Tagging, the confirmation of the question type, the extarction of keywords and extending, the confirmation of the knowledge unit, Through these works, the intention of the user is holded, which greatly helped the last work of this system. The FAQ-based question similarity match model is implemented by sematic sentence similarity computation, which is improved by our system, this model can answer frequently-asked question fastly and concisely.The document warehouse-base automatic answer fetching model fistly deal with the document warehouse beforehand and construct inversed index, then use high efficient information retrieval model to search in the base and return some relevant documents, lastly, we use answer extraction technique to get the answer from these relevant documents and present it to users. For the question which can not be answered by FAQ base, This model can automaticly return exact answer fastly.