Research on Entity Naming Algorithm for NLP Based on Multi-level Fusion Recurrent Neural Network

Natural language processing (NLP) entity naming is one of the important contents of NLP. Its function is to extract information with practical meaning from the text so the system can perform high-level analysis. Due to the polysemy of words in traditional language texts, the texts in long, difficult sentences and complex sentences are difficult to be recognized by machines and accurately, which brings certain troubles to the current program naming algorithm. Therefore, it is necessary to design a new NLP entity naming algorithm, through the deep learning algorithm analysis of the language text, fully integrate it so it can be parsed and named by the computer language. This paper first analyzes the problem of entity naming in NLP and introduces the neural network architecture and supporting attention mechanism. It can better recognize natural language named entities.