The Semantic Annotations Based On T-CRFs Model In The Application Of Intelligent Question Answering System Research

Intelligent Answers is a hot topic in the field of natural language processing. Intelligent Question-Answer System can understand natural language questioning from the user while provide users a simple, accurate answers, rather than the number of related pages. Along with the WeChat, the demand that intelligent question-and-answer service instead of artificial customer becomes more and more strong. And presently intelligence Q&A in the market is implemented based on keywords, keyword stuffing, which couldn’t understand user’s intent also couldn’t achieve the purpose of precise answer. This study could help Q&A system achieve precise answer and improve the efficiency of the quiz questions in some areas.Based on previous studies, using dependency parsing, extracting the dependency tree of parent and child nodes level features, for training in the T-CRFs model, so that the results on the basis of the word mark and speech characteristics have a certain level of the degree of improvement. Experiment chooses Chinese FrameNet knowledge Ⅲbase of the "buy" sentence base from Shanghai hao wen jiao network technology co., LTD, joining the features related to the parent and child node optimal template, automatic tagging elements results accuracy can reach to84.3%, recall rate is62.0%, and the F value is71.5%. And "buy" the framework of areas related to intelligent question answering system results in accurate rate and the rate of the above answers are significantly improved.Against the above problems, this paper studies directly from semantic annotation improvements. Semantic annotation system plays a crucial role in matching quiz questions and answering extraction process, at the same time it is also a key process when intelligent question answering system understand the user’s intent and answer accurately. Based on T-CRFs model, introducing dependency analysis, the article makes Chinese FrameNet semantic annotation for knowledge of the Chinese language. Then we have significantly improved in accuracy, recall and F-Chinese framework applied to intelligent network Knowledge Q&A systems to verify the accuracy rate and answers efficiency. The Chinese FrameNet knowledge base of Chinese frame semantic tagging applied to intelligent question answering system, Q&A system can make understanding user’s intent, and fast give precise answers. This will bring the few of Internet businesses depending on Internet an opportunity to communicate directly with customers and save artificial cost of customer service. While dependency parsing and T-CRFs model combining is used to train knowledge base data, which not only can make the shallow semantic annotation results display very well, but also provide the method that an in-depth development for deep semantic analysis.