# Chinese Named Entity Recognition Model Based on Multi-Task Learning
## 1 Introduction  
Named Entity Recognition (NER) is a crucial task in natural language processing that involves detecting entity boundaries in a sequence and classifying them into predefined entity classes based on contextual semantic understanding. It has many applications in information extraction, automatic knowledge base construction, and question-answering systems, among others [1]. In recent years, deep learning models have shown great success in mining contextual semantic features, greatly advancing the field of named entity recognition. Named entity recognition (NER) is a task that involves two subtasks: predicting entity boundaries and identifying entity types. The evaluation of NER performance requires accurate prediction of both entity boundaries and types. In Chinese, unit vocabulary is more semantically complex and entity boundaries are more ambiguous compared to English, making entity recognition more challenging. Previous research has proposed various solutions to this problem, including incorporating lexicon information into NER models. One approach is to perform word segmentation using a tool and then annotate the resulting sequence of words. However, the effectiveness of this approach depends on the accuracy of word segmentation, particularly in specialized fields where errors can propagate and become more problematic. To address this limitation, researchers have proposed various methods for fusing lexical information with models. For example, [PERSON] et al. [2] proposed a raster-like long- and short-term memory network structure that combines character-level and potential word-level information in the text. [PERSON] et al. [3] used convolutionalneural networks and attention mechanisms to fuse word vectors at different layers, which improved computational efficiency and accelerated model training. Despite the improvement that NER models incorporating lexical information bring to the recognition of Chinese named entities, these models suffer from some drawbacks due to their reliance on a single-objective approach, in which model parameters are shared across boundary prediction and type recognition. One issue with this approach is that type recognition typically requires deeper and more extensive contextual semantic information than boundary prediction. However, the single-objective task encodes both entity boundary and type information uniformly, leading to mixed semantic results for both tasks and making it difficult to learn based on inter-task variability. Another drawback of the single-objective task is its tendency to spend more time on redundant label prediction. For example, in the BIESO sequence labeling approach, the decoding labels for each word in the single-target task network include five options, namely {'O', 'B-entity', 'I-entity', 'E-entity', 'S-entity'}. If there are x entity types, this leads to \(4\)x + 1 combinations of decoding labels for each word. By contrast, splitting the single-target task into two subtasks, namely entity boundary labeling and type labeling, can reduce the number of decoded label combinations per word to x + 5 cases, which is \(3\)x \(-\) 4 fewer options than the single-target task. However, because the NER task requires one annotation per word, learning becomes particularly challenging when there are more entity types or a larger dataset, and the single-target task becomes more disadvantageous. In this paper, we propose a solution to the issues raised in previous studies by decomposing the entity boundary prediction task from the single-objective task and designing a multi-task learning network framework together with the entity type recognition task. Specifically, we decompose the entity recognition task into two subtasks, boundary labeling and type labeling, and construct a backbone network for learning, consisting of a coding layer with shared parameters and a decoding layer with exclusive parameters. Joint learning of the coding layer enhances the model's generalization performance, while the decoding layer mines unique data features for different tasks.