# Chinese Fine-Grained Named Entity Recognition Based on BILTAR and GlobalPointer Modules
## 1 Introduction
The entity boundary score matrix contains the confidence degree of each boundary for each category, and the module obtains the corresponding category of each entity boundary through the confidence degree, thus identifying entities and entity categories. To address these issues, this paper proposes a Chinese fine-grained entity recognition model, Bert-ATT-BILTAR-GlobalPointer. In this model, a multi-position attention mechanism is added to denoise the data. The model in this paper extracts deep semantic information from the text to compensate for the shortcomings of BERT performance by the BILTAR module. The model adds rotation position encoding in the GlobalPointer module to increase the position information, and the GlobalPointer module of the model uses an upper triangular matrix to calculate the scores of all possible entity boundaries in each category. In comparative experiments, the performance of the proposed model is higher than that of recent baseline models. In ablation experiments, it is demonstrated that each submodule of the proposed model can improve the model's performance among various module combinations. The innovation points of this paper are described as follows: (1) Replacing the CRF in the classical entity recognition model with the GlobalPointer module and adding the rotation position encoding (RoPE); (2) The multi-head self-attention mechanism (ATT) with multiple positions; (3) The application of a positive-negative direction module (PN) and time-step module (TIME) in BILSTM; (4) We added the corresponding innovation modules to different module combinations, so as to carry out ablation experiments on each innovation point of the model. The results of the ablation experiments demonstrate the effectiveness of all innovations in this paper. There are 18 groups of ablation experiments in the ablation experiment chapter. In real life, text content generally covers a wide range. Each piece of text may contain several different types of content. In order to improve the recognition ability of the entity recognition model for multiple fine-grained categories, the CLUENER2020 dataset was selected as the first experimental dataset. The CLUENER2020 dataset contains 10 entity categories, covering the vast majority of real-life text content. On the Internet, the general communication information is network language. Such text has the characteristics of being short, random, fuzzy, having a complex context, and so on. In order to investigate the ability of processing network language, we chose a microblog dataset as the second experimental dataset. The Weibo dataset contains four categories and marks whether the entity is explicitly or generally referred to. This paper is divided into five chapters, namely, introduction, related work, model, experiment, conclusion, and future work. Structurally, this paper first introduces the current status and significance of entity recognition research in the introduction and related work sections. Then, we describe the specific details of the model in the model section. The effectiveness of the model is analyzed through the experimental section. Finally, the main work of this paper and future prospects of entity recognition research are summarized in the conclusion and future work section.