# Label-Guided Data Augmentation for Chinese Named Entity Recognition
## 1 Introduction  
Chinese named entity recognition (NER) is an important task in natural language processing (NLP) that identifies and categorizes entities in text into predefined categories. This task is critical to applications such as information extraction, machine translation, and question-answering systems, significantly improving the efficiency and accuracy of text processing. Despite its importance, achieving high performance in NER often requires a large number of high-quality annotated data [1]. However, acquiring such data is particularly challenging in low-resource domains or languages, as it demands extensive domain knowledge, expert annotation, and significant cost. This data scarcity often results in poor model performance due to the insufficient learning of hidden feature representations. Data augmentation offers a promising solution by expanding training samples through operations such as deletion, replacement, insertion, and back translation [2]. While data augmentation has proven effective in various NLP tasks, like sentence classification [3], machine translation [4], and dialogue generation [5], its application to NER presents unique challenges. Unlike other tasks where semantic labels remain unchanged, NER requires precise label predictions at the character level. Traditional augmentation methods, such as back translation, can disrupt character-level semantics and lead to misaligned entity labels, thereby reducing prediction accuracy [6]. To address these issues, recent studies have explored incorporating label semantics as additional input signals [7,8] or leveraging fine-grained entity annotation types to guide data augmentation [9]. While these methods improve the model's understanding of text semantics, they often rely on English-centric datasets, which are scarce in other languages and are usually of smaller scale. Additionally, current approaches that concatenate label semantic embeddings with original sample embeddings fail to effectively guide the learning process and may unnecessarily increase input length. Furthermore, low-resource entity recognition tasks are hindered by class distribution imbalances, making it difficult for models to learn from infrequent classes. To address these challenges, we propose LGDA, a novel Chinese NER model that leverages Label-Guided Data Augmentation. The LGDA model consists of three core components: a data augmentation module, a label semantic fusion module, and an optimized loss function. LGDA operates in two stages. In the first stage, a masked entity generation language model enhances the data by embedding entity labels into the original samples to sharpen Chinese entity boundaries. The model also replaces original entities with newly generated ones, thereby expanding the training set. In the second stage, label annotations are introduced as domain-specific prior knowledge and integrated into the model alongside the augmented and original samples. Twin encoders with shared parameters process the samples and label annotations, and a cross-attention mechanism fuses these inputs, enabling the model to learn representations enriched with label semantics. Additionally, an optimized loss function adaptively assigns higher weights to samples from less distinguishable classes, addressing the long-tail distribution issue in low-resource scenarios. To evaluate the proposed method, extensive experiments were conducted on two public datasets: OntoNotes 4.0 (Chinese) and MSRA. The results demonstrate that LGDA outperforms competitive baselines, achieving state-of-the-art performance. Notably, LGDA excels in few-shot scenarios, where the data augmentation module proves especially effective. In summary, the main contributions of this paper are as follows:  
* We propose a novel Chinese NER model, LGDA, which introduces Label-Guided Data Augmentation to enhance performance in low-resource scenarios.