English Text Named Entity Recognition Method by Fusing Local and Global Features

Abstract

Because of the ambiguity and dynamic nature of natural language, the research of named entity recognition is very challenging. As an international language, English plays an important role in the fields of science and technology, finance and business. Therefore, the early named entity recognition technology is mainly based on English, which is often used to identify the names of people, places and organizations in the text. International conferences in the field of natural language processing, such as CoNLL, MUC, and ACE, have identified named entity recognition as a specific evaluation task, and the relevant research uses evaluation corpus from English-language media organizations such as the Wall Street Journal, the New York Times, and Wikipedia. The research of named entity recognition on relevant data has achieved good results. Aiming at the sparse distribution of entities in text, a model combining local and global features is proposed. The model takes a single English character as input, and uses the local feature layer composed of local attention and convolution to process the text pieceby way of sliding window to construct the corresponding local features. In addition, the self-attention mechanism is used to generate the global features of the text to improve the recognition effect of the model on long sentences. Experiments on three data sets, Resume, MSRA and Weibo, show that the proposed method can effectively improve the model's recognition of English named entities.