# PNER: Applying the Pipeline Method to Resolve Nested Issues in Named Entity Recognition
## 1 Introduction  
In natural language processing (NLP), the identification of specific words or phrases, such as individual names, locations, and organizations, is essential for tasks including machine translation [1], question answering [2, 3], entity linking [4], and relation extraction [5]. These terms, known as named entities (NEs), are identified through named entity recognition (NER), a foundational task in NLP. However, certain texts contain named entities that are nested or overlapping, giving rise to what is known as nested entities or entity nesting [6]. These complexities are prevalent in particular domain-specific datasets. Most researchers concentrate on resolving flat entities [7]; the existence of nested entities in text often goes unaddressed. Refer to Figure 1 for an illustrative sentence extracted from the GENIA dataset [8]: \"Mouse interleukin-2 receptor alpha gene expression\". In this sentence, \"interleukin-2\" is categorized as a \"protein\" entity type. Notably, \"interleukin-2\" also exists within the \"DNA\" entity type \"Mouse interleukin-2 receptor alpha gene\". This scenario exemplifies a nested entity, where \"interleukin-2\" holds multiple entity-type associations within the same text span. Sequence labeling methods are commonly employed to tackle NER tasks. However, these methods confine each token to representing only one entity category, disregarding the possibility of multiple entity categories associated with a single token within nested entities. Addressing this issue, some researchers proposed solutions such as the \"merged label layer\" [9], a method involving sequence labeling models annotating multiple entity categories on tokens with nesting challenges. Although straightforward, this method leads to label expansion, resulting in model sparsity. In our study, we address the significant challenges inherent in processing nested entities with sequence labeling methods, a task complicated by the intricate hierarchies and overlapping entities in text data. Traditional sequence labeling approaches primarily focus on identifying single-layer entities, often inadequately handling or entirely overlooking nested structures. Although some researchers have explored solutions to this problem, their methods either deviate from the simplicity of the sequence labeling framework or require the annotation of an extensive number of labels, leading to model sparsity. Our work seeks to navigate these issues, proposing an innovative approach that retains the simplicity of sequence labeling models while effectively managing the complexity of nested entities. Our research endeavors to optimize this approach by mitigating label proliferation. We devised a pipeline approach to tackle nested entity complexities. This method consists of two stages. Initially, we utilized a sequence labeling model incorporating a merged label layer; however, we streamlined entity categories by introducing a hierarchical entity level. This hierarchical level, labeled as ent, sub1, sub2, and so forth, is determined based on primary and secondary relationships within nested entities, with the outermost entity assigned the highest importance (ent). This hierarchical structure significantly reduces label volume. Subsequently, we employed a classification model to ascertain the entity category. In a prior state-of-the-art model for entity relationship extraction proposed by [PERSON] et al. [10], a pipeline approach was utilized. They proposed an enhancement involving entity boundaries and types as identifiers around entity spans within the classification model. Building upon this notion, our research incorporates entity levels as identifiers around entity spans within the classification model. We hypothesize that entity levels may exert influence on the entity category. We conducted experiments on three datasets encompassing nested entity challenges across diverse languages: the Chinese medical dataset CMeEE [11], the English medical dataset GENIA [8], and the German dataset GermEval2014 [12].