###### Abstract
The advent of deep learning has revolutionized the field such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), and long short-term memory (LSTM) for NER tasks [9; 10; 11], significantly improving both the efficiency and recognition accuracy. Notably, the BiLSTM-CRF model has gained widespread use [12; 13; 14; 15] as it automates feature extraction, thereby enhancing both efficiency and accuracy without the large scale feature engineering required by earlier methods [16]. In recent years, several tools have been developed to enhance NER tasks. SpaCy, a widely-used Python library, offers pre-trained models for the efficient processing and customization of domain-specific tasks including biomedical entities [17]. NLTK, while primarily research-focused, provides a flexible platform with sequence labeling tools, making it well-suited for early-stage NER implementations [18]. Stanford NLP offers a broad range of natural language processing tools [19], with RegexNER standing out as a rule-based tool that leverages regular expressions, ideal for identifying well-defined entity patterns such as chemical names and drug abbreviations [20]. More recently, advanced libraries like Flair have further improved NER performance by incorporating contextualized embeddings like BERT [21], which capture deeper semantic relationships within text. These tools, along with deep learning models, particularly transformer-based architectures, are advancing NER, significantly boosting accuracy in both general tasks and specialized applications like BioNER. As research in NER has advanced, increasing attention has been directed toward the recognition of nested entities. The concept of \"nested region names\" was first introduced in the task definition of automatic content extraction (ACE) research [22], represented as entities that exist within other entities [23]. For instance, in the study of chicken diseases, a symptom such as'spleen swelling' includes the nested entities'spleen', representing a body part, and'swelling', representing a symptom. Recognizing these nested entities, whether they are heterogeneous (different types) or homogeneous (overlapping types), is complex and presents a significant challenge for accurate identification [24]. The introduction of the concept of \"nested region names\" in ACE research underscores the importance of recognizing these intricate structures within named entities. To address the challenges posed by nested named entities, researchers have proposed various methods including hypergraph-based, region-based, transition-based, and span-based techniques. For example, [PERSON] et al. [25] introduced a hyper graph network (HGN) structure to manage nested entities by representing each sentence as a hypergraph, with words as nodes and entities as hyper-edges, thereby transforming the recognition task into hyper-edge classification. Region-based methods treat nested NER as a multi-class classification problem by first representing potential regions (subsequences) and then classifying them. [PERSON] et al. [26] proposed a candidate region-aware model that utilized binary sequence labeling followed by candidate region classification, demonstrating significant performance on public datasets. Transition-based methods, inspired by dependency parsers, incrementally construct trees through greedy decoding. [PERSON] et al. [27] developed a neural transition model using Stack LSTM, which effectively captured character-level representations and efficiently represented the system's state. Span-based methods enumerate all possible text segments (spans) and then determine their entity status [28], which naturally suits the nested entity recognition task. [PERSON] et al. [29] enhanced this approach by developing a segment-enhanced span-based model (SESNER) that improved the model performance while accurately handling complex nested entities.