# Military Equipment Entity Extraction Based on Large Language Model
## 2 Related Works  
Entity extraction is a fundamental task in the field of natural language processing, aiming to extract entities with specific meanings from unstructured text. Based on the complexity and types of the extraction objects, entity extraction can be categorized into simple entity extraction and complex entity extraction. Simple entity extraction primarily targets flat entity types, which usually have relatively fixed formats and clear contexts. Complex entity extraction, on the other hand, focuses on extracting entities that are complex, diverse, and highly context-dependent, including nested and discontinuous entities. The methods for entity extraction have undergone three main stages of development. The first stage involves domain-specific entity extraction, which focuses on fixed expressions within a domain by formulating domain rules based on data characteristics. [PERSON] et al. [7] proposed a representative method called DL-CoTrain. This method first manually constructs decision lists, then, performs unsupervised learning on the dataset to build more rule sets, and finally, classifies these rule sets. The effectiveness of this method is positively correlated with the quality and quantity of the manually constructed rule sets. Consequently, such methods are highly sensitive to changes in new data and have poor transferability. With the development of machine learning, simple entity extraction entered the second stage, where entity extraction was treated as a sequence labeling problem. This involved building models and manually extracting features to label each word in the text, thereby achieving entity extraction. [PERSON] et al. [2018] were the first to apply the maximum entropy model to the entity extraction task, combining rule-based syntax with the maximum entropy model. This method incurred significant overhead due to the need for explicit normalization calculations. Subsequently, [PERSON] et al. [2018] proposed a standard Hidden Markov Model (HMM), which directly modeled transition and emission probabilities, reducing computational overhead by statistically modeling co-occurrence probabilities. However, these methods normalized only locally, which could lead to local optima. The Conditional Random Field (CRF) model, which statistically considers global probabilities and normalizes the entire data distribution, was introduced into entity extraction by [PERSON] and [PERSON] [2018], achieving good results. These methods have some transferability but require manual feature selection, placing demands on the feature selection capabilities of relevant personnel. With the advancement of deep learning technology, entity extraction has entered the third stage, based on deep neural networks. Deep learning-based entity extraction typically employs models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Bidirectional Long Short-Term Memory (BiLSTM) networks, and Transformers, often combined with CRF for joint training to improve extraction accuracy. [PERSON] et al. [2018] were the first to use CNNs to embed each input word and employed CRF to decode each embedding into specific entities. Unlike fully connected neural networks that require fixed input lengths, RNNs consist of recurrent units that can handle variable-length data, making them more suitable for sequential inputs like text data. However, RNNs face issues of gradient vanishing or exploding during backpropagation when dealing with long sequences, which severely affect model training. BiLSTM introduces gating mechanisms to control information flow, allowing the forgetting of redundant information, making it more suitable for long sequence scenarios. [PERSON] et al. [2018] used BiLSTM to enhance memory of effective information and employed CRF for globally optimal prediction of label sequences. These methods perform well on flat text but are limited in performance when dealing with nested or discontinuous complex entities. [PERSON] et al.