## 2 Related Work
### Multi-Label Text Recognition  
Multi-label recognition of legal texts represents a specialized subdomain within multi-label classification, necessitating the precise identification of domain-specific terminologies and hierarchical structures from lengthy legal documents. Compared with general multi-label recognition tasks, legal text classification is distinguished by its strict sentence structures, systematic semantic tagging, specialized domain-specific terminology requiring expert interpretation, and long-tailed data distributions. These characteristics render legal text classification more challenging and complex than general tasks. Current research in this domain can be categorized into three primary approaches based on hierarchical traversal methods: flat methods, local methods, and global methods. The flat method simplifies the hierarchical multi-label classification problem into a standard multi-label classification task. This approach assumes mutual independence between labels at different hierarchical levels, flattening them into a single-layer label prediction or focusing solely on terminal-level label prediction. For instance, [PERSON] et al. [19] integrated TextCNN, RNN, and attention-based capsule networks to optimize classification networks for multi-label tasks. [PERSON] et al. [20] proposed XML-CNN, which incorporates bottleneck layers and dynamic max-pooling to enhance hierarchical label recognition. However, this assumption disregards the inherent hierarchical structure of legal labels. In addition, the resulting label predictions fail to capture inter-label hierarchical dependencies, yielding suboptimal practical classification accuracy. The local method utilizes independent classifiers for different hierarchical levels, progressively predicting labels from lower to higher levels. For instance, [PERSON] et al. [21] developed a hierarchical support vector machine (HSVM) that constructs separate SVM classifiers for each level and integrates discriminant functions to maintain hierarchical consistency. [PERSON] et al. [22] utilized multiple neural networks independently trained through transfer learning to enhance hierarchical classification. [PERSON] et al. [23] introduced a hierarchical multi-label classification network (HMCN) that jointly models local classification dependencies and global hierarchical information, optimizing classification at local and global levels. However, this method propagates classification errors from lower levels upward, increasing uncertainty in higher-level predictions. In addition, it amplifies erroneous predictions at intermediate levels, leading to a gradual decline in prediction accuracy as the hierarchy ascends, an undesirable phenomenon in which classification performance deteriorates progressively across hierarchical tiers. Global methods typically involve a single classifier that fully integrates hierarchical category information into the classification process, designing optimization strategies to capture hierarchical label relationships and enabling direct prediction of hierarchical labels [24]. [PERSON] et al. [5] proposed the hierarchy-aware global model (HiAGM) for hierarchical label prediction, which combines a bidirectional tree long short-term memory network (Bi-TreeLSTM) with a graph convolutional network (GCN) to model label hierarchical relationships. [PERSON] et al. [6] developed a Hi-Match network that models text and hierarchical multi-label semantics. They transformed label recognition into a semantic matching problem, incorporating hierarchical information by calculating the semantic similarity between texts and hierarchical labels. As such, they achieved hierarchical label identification. [PERSON] et al. [25] addressed the long-tail distribution challenge of last-level labels by proposing the HTCinfoMax model, which introduces text-label mutual information maximization and prior label matching to filter irrelevant information. [PERSON] et al.