Entity generation algorithm based on reference expansion

The extraction and understanding of text knowledge become increasingly crucial in the age of big data. One of the current research areas in the field of natural language processing (NLP) is how to accurately understand the text and collect accurate linguistic information because Chinese vocabulary is diverse and ambiguous. This paper mainly studies the candidate entity generation module of the entity link system. The candidate entity generation module constructs an entity reference expansion algorithm to improve the recall rate of candidate entities. In order to improve the efficiency of the connection algorithm of the entire system while ensuring the recall rate of candidate entities, we design a graph model filtering algorithm that fuses shallow semantic information to filter the list of candidate entities, and verify and analyze the efficiency of the algorithm through experiments. By analyzing the related technology of the entity linking algorithm, we study the related technology of candidate entity generation and entity disambiguation, improve the traditional entity linking algorithm, and give an innovative and practical entity linking model. The recall rate exceeds 82%, and the link accuracy rate exceeds 73%. Efficient and accurate entity linking can help machines to better understand text semantics, further promoting the development of NLP and improving the users’ knowledge acquisition experience on the text.