# Named Entity Recognition in the Field of Small Sample Electric Submersible Pump Based on FLAT
## 2 Materials and Methods  
### Related Work on NER  
The origin of NER dates back to the 1970s, when early advancements in computer technology led researchers to explore extracting structured information from text, particularly in specific domains like news reports and the scientific literature. Early NER methods relied on manually defined rules and dictionaries. These methods performed well in specific domains and small-scale texts [29].  
From the late 1990s to the early 2000s, statistical methods like the Hidden Markov Model (HMM) [30], and Support Vector Machine (SVM) [31] were increasingly used for sequence labeling in NER tasks. Among these, Conditional Random Field (CRF) [32] became the mainstream approach due to its ability to capture contextual information, leading to significant performance improvements. However, traditional machine learning methods rely heavily on feature engineering, requiring manual design of feature templates. This is not only inefficient but also prone to errors due to the lack of prior knowledge.  
Mainstream NER algorithms are mostly based on deep learning methods, which automatically extract features to avoid the complex feature engineering required in traditional machine learning. Common deep learning architectures include Recurrent Neural Network (RNN) [33], Convolutional Neural Network (CNN) [34], and Transformer architectures. Among these, the architecture combining Bidirectional Long Short-Term Memory networks with Conditional Random Fields (BiLSTM-CRF) [35] has become widely used in NER tasks. BiLSTM processes sequential data bidirectionally to capture bidirectional contextual information in text, while the CRF layer optimizes the sequence labeling results, significantly enhancing the performance of NER. Additionally, pre-trained language models such as BERT and Generative Pre-trained Transformer (GPT) [36], which undergo large-scale unsupervised pre-training, further enhance the models' language understanding capabilities and have achieved remarkable results in NER tasks. These models can quickly adapt to specific tasks through fine-tuning without the need for training from scratch. The advantage of deep learning methods lies in their ability to automatically extract features, which not only improves efficiency but also reduces the need for manual involvement from linguistic experts.  
However, deep learning methods typically require large amounts of high-quality data and annotations, making it difficult to achieve rapid migration and application in specific domains with scarce high-quality corpora, as well as in different languages. In recent years, domain-specific NER research has become the foundation for many scientific projects and has attracted widespread attention from researchers. [PERSON] et al. [37] used a neural text generation method to increase corpus data through generative adversarial networks, achieving NER in Chinese electronic medical records [38; 39]. [PERSON] et al. [40] adopted a deep learning method based on multi-feature fusion to recognize complex entities in unstructured shipping enterprise credit texts, providing a foundation for constructing a credit knowledge graph for shipping enterprises. These NER studies cover a variety of fields, including medical, biological, aviation, and mechanical domains, focusing on the recognition of complex entities within these specific fields.  
Building on the experimental achievements of these researchers, this paper makes it possible to conduct low-resource NER in the ESP well domain.