## 4 Experiments
### Datasets  
We begin with an introduction to the five datasets used in the experiment:  
ACE2004 [38] is a dataset issued by the Language Data Consortium (LDC) and exists in three types, English, Arabic, and Chinese, of which we have chosen the English type. This dataset aims to develop automatic content extraction techniques to support the automated processing of human language in a textual form. It can be used for nested entity recognition in NER.  
ACE2005 [39] is also a dataset published by LDC and includes English, Arabic, and Chinese training datasets. We chose the English-type dataset. This dataset can be used for nested NER and relation extraction tasks. There are 145,000 English words in the dataset with six entity types: person name, organization name, location name, time, currency, and percentage.  
The GENIA [25] dataset was extracted from the biomedical literature, which informs the development of bio-text mining systems. The dataset contains 1999 abstracts from medical literature analysis and retrieval systems. The abstracts are collected using three medical subject terms: human, blood cell, and copy factor. It can be used to develop and evaluate natural language processing (NLP) algorithms and tools, such as text classification and named entity recognition.  
MSRA [40] is a dataset on Chinese NER from Microsoft Research Asia (MSRA), which contains over 50,000 Chinese entity recognition annotations in the following entity categories: place name, institution name, and person name.  
Resume [41] is a CV-oriented Chinese NER dataset published by Purdue University, which contains eight entity types, including country (CONT), educational background (EDU), location name (LOC), person name (NAME), organization name (ORG), profession (PRO), race (RACE), and title (TITLE).  
[PERSON] [PERSON] et al. [42]: detection and identification of entities on multi-granularity feature information.  
[PERSON] et al. [43]: NER via dynamic span diagrams.  
[PERSON] et al. [44]: linearization of multiple labels of nested entities into one label followed by sequence labeling methods.  
[PERSON] et al. [33]: a span-based nested entity recognition model using boundary enhancement.  
[PERSON] et al. [45]: a view-based nested NER as constituency parsing with partially observed trees.  
[PERSON] et al. [13]: nested entity recognition using a stacked model in the shape of a pyramid.  
[PERSON] et al. [35]: construction of nested entity recognition models for span representation using additive attention mechanisms.  
[PERSON] et al. [34]: training using biaffine attention mechanisms and multi-task learning for boundary recognition.  
[PERSON] and [PERSON] [41]: a model for Chinese entity recognition tasks that explicitly utilizes word information and word order information is proposed.  
[PERSON] et al. [46]: an NER framework using an adaptive transformer encoder is proposed for modeling character-level features and word-level features.  
[PERSON] et al. [47]: a dictionary-based graph neural network model is used to deal with the Chinese entity recognition problem.  
[PERSON] et al. [48]: a multi-level CNN was constructed to capture both short-term and long-term contextual information for entity recognition.  
[PERSON] et al. [49]: A new lexicon enhancement is proposed that can effectively improve the problems of excessive memory and computational costs caused by the previous use of lexicons.