# PNER: Applying the Pipeline Method to Resolve Nested Issues in Named Entity Recognition
## 2 Related Work  
Named entities, a concept first introduced by [PERSON] and [PERSON] in 1996 [14], have played a pivotal role in the field of natural language processing. The development and refinement of named entity datasets since then have been instrumental in advancing the domain of flat named entity recognition (NER) [15]. The most widely adopted model in NER has been the sequence labeling model [7], with the LSTM-CRF model standing out as a classic example of this approach [16; 17], where long short-term memory (LSTM) is a specialized form of recurrent neural networks (RNNs) introduced by [PERSON] and [PERSON] in 1996 [18]. It features a unique memory cell designed to retain past information over extended periods. In sequence tagging tasks, to ensure that both past and future input features are recognized, a bidirectional LSTM network is utilized for named entity recognition tasks [19]. [PERSON] (2007) presented a variety of strategies for integrating multiple conditional random fields (CRF) [20] to address the tasks [21]. Through fine-tuning, the pre-trained model BERT can be adapted for various tasks, including named entity recognition. The subsequent introduction of the pre-trained model BERT marked a significant leap forward in NER efficiency and accuracy. In the evaluation task of the CMeEE dataset, various versions of BERT were employed as benchmarks for assessment [22]. Furthermore, various labeling schemes like BIO, BIOES, and BILOU have emerged, evolving from these sequence labeling models and further enriching the tools available for NER tasks. In the realm of biology, researchers have identified a unique challenge with certain entities that demonstrate nesting issues, where one entity encompasses other entities. Tackling these nested entity problems initially posed significant challenges, but over time, various innovative methods have been proposed to address them. A notable solution is the cascading model, pioneered by [PERSON] et al. [16]. This model adopts a hierarchical strategy, building multiple layers of flat NER recognition that systematically identify entities from the innermost layer outward until no further entities are detectable. This approach ensures that information about inner entities is fully utilized by outer entities. However, it has a limitation in the one-way flow of information, as it does not effectively harness information from outer entities. Consequently, when misidentifications occur at the innermost levels, they can result in cascading errors throughout the system. To overcome this limitation, [PERSON] (2020) implemented neural network techniques to facilitate a mutual interaction between inner and outer entities [23]. This innovation marked a significant advancement in handling nested entities. Additionally, [PERSON] (2020) introduced a pyramid structure to refine and optimize the concept of the hierarchical model [24], further enhancing the effectiveness of nested entity recognition. Several researchers have innovated named entity recognition (NER) by proposing the multi-head approach. In this method, token pairs, comprising head and tail tokens, define a span. A multi-head matrix is then constructed using various techniques like dot-product scoring [25], additive methods [26], and a blend of multiplicative and additive approaches [27]. This approach also integrates the concept of Biaffine [28] into the field. Another significant advancement is the span-based method. This technique identifies all entities within a sequence by considering every possible span of characters in the text, employing a neural network-based classifier for this purpose [29; 30]. An alternative perspective treats NER as a reading comprehension task [31]. This involves incorporating entity category information into the input, presenting a unified framework suitable for a range of information extraction tasks [32]. Finally, traditional sequence labeling methods struggle with nested entities.