# Chinese Named Entity Recognition Based on Multi-Level Representation Learning
## 1 Introduction  
The development of NER models has progressed from traditional rule-based and statistical approaches [1] to modern deep learning methods [2]. NER plays a vital role in various applications such as information extraction [3], question answering systems, machine translation [4], and sentiment analysis. In the embedding layer, early word vector [5] representation methods have been gradually replaced by contextual embedding methods [6], which better capture complex contextual semantic information. In terms of feature fusion, models have evolved from single-feature models to multi-feature fusion models, combining character features, word features, and syntactic features at multiple levels to enhance NER performance [7, 8]. For decoding, traditional methods [9] have been gradually replaced by approaches that incorporate neural networks [10], improving the ability to capture dependencies between labels. In recent years, pointer-network-based decoders [11] have also been proposed and have achieved excellent results in NER. However, while existing models perform well in certain areas, they still have some limitations: (1) challenges in recognizing polysemous words and ambiguities in complex Chinese texts; (2) difficulties in capturing long-range dependencies; and (3) poor robustness to noisy data, which limits their effectiveness in practical applications. For instance, [PERSON] et al. [12] proposed a BiLSTM-CRF-based model that captures contextual information through BiLSTM and employs a CRF layer for sequence labeling, effectively identifying entity boundaries. This model, however, struggles when dealing with polysemouswords and ambiguities in complex texts, and the CRF layer incurs high computational costs when handling a large number of labels, which restricts its scalability in large datasets. To reduce the computational cost of CRF, pointer networks were introduced to NER tasks. [PERSON] et al. [13] proposed a pointer-network-based model that encodes input sequences and locates entity boundaries via a pointer mechanism, simplifying the computation process. Nevertheless, pointer networks tend to perform inadequately when capturing long-range dependencies and handling complex sequences. [PERSON] et al. [14] addressed this by incorporating multi-head attention (MHA) to improve global context understanding and long-range dependency capture. Yet, MHA's fixed attention head weights limit its dynamic adaptability to different input features, and the inability to adjust attention head importance during task execution may waste computational resources, reducing overall model efficiency. Additionally, noise in data processing poses challenges for model robustness. To address these issues, this paper proposes a Chinese NER model based on multi-level representation learning, named WP-XAG. The primary purpose of this research is to enhance the accuracy and robustness of Chinese NER models. The specific tasks to achieve this purpose include:  
1. Using a word-based pre-trained model, WoBERT, which effectively addresses the challenges of polysemy and word-sense disambiguation for initial contextual embedding. WoBERT is particularly advantageous due to its extensive pre-training on large corpora, allowing it to capture the nuanced meanings of words in context. This capability is crucial for recognizing entities that may have multiple interpretations, thereby directly contributing to improved NER performance in the complex landscape of the Chinese language. Additionally, we incorporate Projected Gradient Descent (PGD) from adversarial training to bolster the model's robustness against data perturbations and noise. 2. Employing an Extended Long Short-Term Memory (XLSTM) network to capture contextual dependencies in the sequence. The XLSTM is chosen for its enhanced ability to model long-range dependencies, essential for comprehending complex sentence structures in Chinese. Moreover, by introducing a normalization layer, we facilitate faster model convergence, thus enhancing training efficiency.