## 2 Related Work  
### Research Status of Information Extraction  
After statistical machine learning algorithms, named entity recognition tasks have entered a new era and are now being integrated with neural networks. Researchers started to use recurrent neural networks (RNN) based on character embedding and word embedding to identify named entities in sentences, solving the feature engineering problem that exists in conventional statistical methods. With the rapid development of deep learning,using deep learning methods to solve named entity recognition problems has become a popular research topic. The advantage of this class of methods is that the neural network model can automatically learn sentence features without complex feature engineering [5]. In word vector-based processing, the text is first divided into words, and then the words are mapped into the form of vectors so that there is a feature vector for each word. The main problems faced by this approach are mainly word division errors leading to named entities being incorrectly sliced, large dimensionality of the lexicon, and the inability to effectively characterize the correlation between similar words [6]. However, the advantages are mostly the word boundary information and semantic features. Bidirectional Long Short-Term Memory (BiLSTM) in NER achieves comprehensive sequence modeling by considering the context of each word, thereby improving the accuracy of named entity recognition. The model possesses the capability to extract sequence features, handle long-distance dependencies, and generate dynamic sequence representations, providing flexibility to the model. By learning advanced language features, BiLSTM performs exceptionally well in NER tasks and has become an effective sequence labeling, similarly, in the early days, to named entity recognition. Later on, feature-based methods are used to classify relationships using machine learning methods with input entities and corresponding textual, syntactic, or language-related features. The effectiveness of the aforementioned methods is greatly influenced by the quality of the features derived from the available natural language processing tools [7]. The emergence of neural networks has introduced a new direction for feature extraction. Many neural networks, including recurrent neural networks, convolutional neural networks (CNN), graph neural networks, and long- and short-term memory networks, are applied in named entity recognition tasks and relationship extraction tasks. In natural language processing (NLP) tasks, text input is usually represented as a sequence of information, and RNN are suitable for processing sequence information and are widely used in NLP tasks. Graph neural networks are suitable for exploiting the relationship between points and point sets [8]. This enables them to effectively capture the structural information of text, and they have also received attention in natural language processing tasks in recent years. [PERSON] et al. [9] introduced the PCNN (segmental convolutional neural network) approach. In the case of relatively small training data, a multi-sample learning approach is employed to allow the presence of mislabeling in the tagged sentences or examples. Additionally, a segmented maximum pooling convolutional architecture is used to automatically learn the relevant features.