# Chinese Fine-Grained Named Entity Recognition Based on BILTAR and GlobalPointer Modules
## 1 Introduction  
Named entity recognition (NER) is a critical task in natural language processing and an essential component in various NLP technologies, such as information extraction, information retrieval [1], machine translation, and question-answering systems [2]. The task of NER is to locate entities in text and predict their categories, providing downstream tasks with rich entity information. There have been many research achievements in coarse-grained entity recognition, and the \(F1\) values of various models have far-exceeded 90%. However, in the real world, it is not enough to use only coarse-grained category information, and in most cases, fine-grained categories are needed to obtain deeper semantic information from the text. For example, sometimes it may be necessary to predict whether an organization belongs to the government or a company. However, fine-grained categories have a downside. Fine-grained categories often have similar categories that are difficult to distinguish, which presents a challenge in predicting fine-grained entity types. For example, when the coarse-grained category \"organization\" is split into \"government\" and \"company\", the two categories are not as easy to distinguish as \"organization\" and\"person name\". Therefore, fine-grained entity recognition is more challenging and valuable for research. The use of simple neural networks such as BERT-CRF [3] may not extract enough semantic information to distinguish between similar fine-grained categories. Entity recognition models need a more powerful feature extraction layer to improve the semantic extraction ability. In the early days, named entity recognition methods often used the English language, and there was little research on Chinese entity recognition. With the widespread application of Chinese named entity recognition (CNER), many studies have been carried out on NER in Chinese, and Chinese NER faces two challenges. The first challenge is that classical sequence labeling methods are based on character-level tagging. The method marks the characters at the specific positions of the entity. One example is the \"B-I-O\" encoding method, which represents \"B\" as the first character of the entity, \"I\" as the internal character of the entity, and \"O\" as the non-entity character. Due to the problem of vanishing gradients over long distances in RNN networks, many models are unable to extract long-distance semantic information and mainly rely on local context for predictions. The second challenge is that in Chinese, there is a lack of boundary information for each character. There are no separators between Chinese characters. But in English, each word is separated by a space. In general, sequential labeling methods do not carry out special labeling and processing of entity boundaries. Chinese entity boundaries are affected by their contextual features and semantic information, resulting in a certain degree of ambiguity. This ambiguity reduces the predictive effectiveness of entity boundaries. At present, most of the classical entity recognition models use CRF to predict entity labels. Conditional random fields (CRF) is a conditional probability distribution model of another set of output sequences given a set of input sequences, which is often used in problems such as annotation and entity recognition. However, this method belongs to the sequence labeling method, and the research based on this method generally conducts corresponding labeling based on characters and lacks the processing of entity boundaries. Therefore, in order to solve the above problems, the GlobalPointer module came into being. The module uses the upper triangular matrix to consider all possible entity boundaries and eliminate the fuzziness of entity boundaries. The GlobalPointer module takes a multi-step matrix product of two eigenvectors and then performs a dimensional transformation of the product result to obtain an entity boundary score matrix.