Named Entity Recognition (NER) is a Natural Language Processing (NLP) technique used to automatically discover and identify entities with specific meanings from text and classify them into predefined categories, such as Person, Organization, Location, Time, Quantity, and domain-specific entities. This technology is one of the fundamental tasks in information extraction, knowledge graph construction, and intelligent search systems.

The development of Named Entity Recognition (NER) is built upon long-term research in multiple fields, including information extraction, statistical natural language processing, and machine learning. Early NER methods primarily relied on manually designed rules, dictionary matching, and statistical models to achieve entity recognition, but their generalization ability was limited in complex contexts and cross-domain tasks. With the development of deep learning, researchers began to utilize neural networks to automatically learn textual context features. In 2016, researchers at Carnegie Mellon University, including Guillaume Lample, published a paper... Neural Architectures for Named Entity Recognition The paper proposes a neural network-based named entity recognition method based on bidirectional LSTM and conditional random field (BiLSTM-CRF), which reduces the reliance on manual feature engineering and has become one of the important representative research works in the field of neural NER.

Modern named entity recognition systems typically combine pre-trained language models (such as BERT), large language models (LLM), and domain adaptation techniques to identify entity boundaries and categories in text. These models are usually evaluated using metrics such as precision, recall, and F1 score, and are widely used in scenarios such as knowledge graph construction, intelligent question answering, financial information analysis, medical text processing, and public opinion analysis, providing crucial technical support for the conversion of unstructured text into structured knowledge.