Research on the Application of BiLSTM-CRF Model in the Field of Chinese Modern History Question-and-Answer

Abstract

Knowledge graph is the key technology of knowledge engineering in the era of big data. Using the powerful semantic understanding and knowledge organization ability of knowledge graph, it can be a better solution to the problems such as the disordered and over-wide coverage of knowledge related to modern Chinese history. The core of this paper is to use high-quality machine learning and deep learning algorithms with the support of big data knowledge graph to obtain the problem analysis result through natural language processing, and then match the problem analysis result with the question template to generate relevant query statements in the constructed knowledge graph to query relevant content through the knowledge graph rich semantic relations. The close relationship between the entities returns the most appropriate information for the user. The experimental results show that the designed question-and-answer system of modern Chinese history fills the gap in this field to a certain extent.