Method Of Chinese Named Entity Recognition Research Articles

A Robust Chinese Named Entity Recognition Method Based on Integrating Dual-Layer Features and CSBERT

In the rapidly evolving field of cybersecurity, the integration of multi-source, heterogeneous, and fragmented data into a coherent knowledge graph has garnered considerable attention. Such a graph elucidates semantic interconnections, thereby facilitating sophisticated analytical decision support. Central to the construction of a cybersecurity knowledge graph is Named Entity Recognition (NER), a critical technology that converts unstructured text into structured data. The efficacy of NER is pivotal, as it directly influences the integrity of the knowledge graph. The task of NER in cybersecurity, particularly within the Chinese linguistic context, presents distinct challenges. Chinese text lacks explicit space delimiters and features complex contextual dependencies, exacerbating the difficulty in discerning and categorizing named entities. These linguistic characteristics contribute to errors in word segmentation and semantic ambiguities, impeding NER accuracy. This paper introduces a novel NER methodology tailored for the Chinese cybersecurity corpus, termed CSBERT-IDCNN-BiLSTM-CRF. This approach harnesses Iterative Dilated Convolutional Neural Networks (IDCNN) for extracting local features, and Bi-directional Long Short-Term Memory networks (BiLSTM) for contextual understanding. It incorporates CSBERT, a pre-trained model adept at processing few-shot data, to derive input feature representations. The process culminates with Conditional Random Fields (CRF) for precise sequence labeling. To compensate for the scarcity of publicly accessible Chinese cybersecurity datasets, this paper synthesizes a bespoke dataset, authenticated by data from the China National Vulnerability Database, processed via the YEDDA annotation tool. Empirical analysis affirms that the proposed CSBERT-IDCNN-BiLSTM-CRF model surpasses existing Chinese NER frameworks, with an F1-score of 87.30% and a precision rate of 85.89%. This marks a significant advancement in the accurate identification of cybersecurity entities in Chinese text, reflecting the model’s robust capability to address the unique challenges presented by the language’s structural intricacies.

A Weakly Supervised Chinese Named Entity Recognition Method Combining First-Order Logic

A Weakly Supervised Chinese Named Entity Recognition Method Combining First-Order Logic

Chinese named entity recognition method based on multiscale feature fusion

Chinese named entity recognition method based on multiscale feature fusion

A Chinese Named Entity Recognition Method Based on ERNIE-BiLSTM-CRF for Food Safety Domain

Food safety is closely related to human health. Therefore, named entity recognition technology is used to extract named entities related to food safety, and building a regulatory knowledge graph in the field of food safety can help relevant authorities to regulate food safety issues and mitigate the hazards caused by food safety problems. However, there is no publicly available named entity recognition dataset in the food safety domain. In contrast, the non-standardized Chinese short texts generated from user comments on the web contain rich implicit information that can help identify named entities in specific domains (e.g., food safety domain) where the corpus is scarce. Therefore, in this paper, named entities related to food safety are extracted from these unstandardized texts on the web. However, the existing Chinese named entity identification methods are mainly for standardized texts. Meanwhile, these unstandardized texts have the following problems: (1) their corpus size is small; (2) there are various new and wrong words and noise; (3) and they do not follow strict syntactic rules. These problems make the recognition of Chinese named entities for online texts more challenging. Therefore, this paper proposes the ERNIE-Adv-BiLSTM-Att-CRF model to improve the recognition of food safety domain entities in unstandardized texts. Specifically, adversarial training is added to the model training as a regularization method to alleviate the influence of noise on the model, while self-attention is added to the BiLSTM-CRF model to capture features that significant impact entity classification and improve the accuracy of entity classification. This paper conducts experiments on the public dataset Weibo NER and the self-built food domain dataset Food. The experimental results show that our model achieves a SOTA performance of 72.64% and a good performance of 69.68% for F1 values on the public and self-built datasets, respectively. The validity and reasonableness of our model are verified. In addition, the paper further analyses the impact of various components and settings on the model. The study has practical implications in the field of food safety.

Chinese named entity recognition method for the finance domain based on enhanced features and pretrained language models

For some named entities in the Chinese finance domain that are long, with difficult to delineate boundaries and diverse forms of expression, we propose a method based on pretrained language models for named entity recognition with enhanced features. First, the method considers entity boundary delineation and entity classification as two separate tasks and learns enhanced Chinese character features by introducing a gating-based multi-channel attention mechanism to delineate financial entity boundaries on the basis of a pretrained language model. Then, the boundary demarcation results are input into the pretrained language model in the form of mask units for data enhancement. Subsequently, document-level entity-based enhancement features are introduced to construct a finance entity classification model. We experimentally identified the best-performing Chinese pretrained language model from several state-of-the-art models and then embedded it into our method to compare against other benchmark models. The experimental results showed that our model is superior to other benchmark models on the named entity recognition task in the finance domain.

Chinese Named Entity Recognition Method Combining ALBERT and a Local Adversarial Training and Adding Attention Mechanism

For Chinese NER tasks, there is very little annotation data available. To increase the dataset, improve the accuracy of Chinese NER task, and improve the model's stability, the authors propose a method to add local adversarial training to the transfer learning model and integrate the attention mechanism. The model uses ALBERT for migration pre-training and adds perturbation factors to the output matrix of the embedding layer to constitute local adversarial training. BILSTM is used to encode the shared and private features of the task, and the attention mechanism is introduced to capture the characters that focus more on the entities. Finally, the best entity annotation is obtained by CRF. Experiments are conducted on People's Daily 2004 and Tsinghua University open-source text classification datasets. The experimental results show that compared with the SOTA model, the F1 values of the proposed method in this paper are improved by 7.32 and 7.98 in the two different datasets, respectively, proving that the accuracy of the method in this paper is improved in the Chinese domain.

A chinese named entity recognition method for small-scale dataset based on lexicon and unlabeled data

A chinese named entity recognition method for small-scale dataset based on lexicon and unlabeled data

Chinese Named Entity Recognition Method in History and Culture Field Based on BERT

With rapid development of the Internet, people have undergone tremendous changes in the way they obtain information. In recent years, knowledge graph is becoming a popular tool for the public to acquire knowledge. For knowledge graph of Chinese history and culture, most researchers adopted traditional named entity recognition methods to extract entity information from unstructured historical text data. However, the traditional named entity recognition method has certain defects, and it is easy to ignore the association between entities. To extract entities from a large amount of historical and cultural information more accurately and efficiently, this paper proposes one named entity recognition model combining Bidirectional Encoder Representations from Transformers and Bidirectional Long Short-Term Memory-Conditional Random Field (BERT-BiLSTM-CRF). First, a BERT pre-trained language model is used to encode a single character to obtain a vector representation corresponding to each character. Then one Bidirectional Long Short-Term Memory (BiLSTM) layer is applied to semantically encode the input text. Finally, the label with the highest probability is output through the Conditional Random Field (CRF) layer to obtain each character’s category. This model uses the Bidirectional Encoder Representations from Transformers (BERT) pre-trained language model to replace the static word vectors trained in the traditional way. In comparison, the BERT pre-trained language model can dynamically generate semantic vectors according to the context of words, which improves the representation ability of word vectors. The experimental results prove that the model proposed in this paper has achieved excellent results in the task of named entity recognition in the field of historical culture. Compared with the existing named entity identification methods, the precision rate, recall rate, and F_1 value have been significantly improved.

A BERT based Chinese Named Entity Recognition method on ASEAN News

As the first step of building a knowledge graph to record the ASEAN counties’ information, we aim to conduct Named-entity Recognition (NER) on the Chinese news about ASEAN counties. We employ a Bi-directional gated recurrent unit to replace the LSTM architecture to improve both models’ effectiveness and capability in understanding polysemous words. The state-of-the-art word embedding model, BERT, has also been included to generate qualified word vectors for the NER task. Besides, we also propose a similarity-based dataset partition method to help model learning the polysemy within the Chinese news. Experiments have been done to demonstrate that the combination of such improvements can benefit the models’ performance in identifying different types of named entities.

使用TB-LSTM-CRF提高工业中文文本实体识别任务

使用TB-LSTM-CRF提高工业中文文本实体识别任务

Research on Methods for Complex Chinese Entity Recognition

Named entity recognition(NER), as a key task of natural language processing, is of great significance in relational capture, information retrieval, knowledge mapping, machine translation, and question and answer systems. In the neural network-based Chinese named entity recognition method, the vectorized word representation only maps words into one vector, which cannot represent the above problem, In order to solve these problems, this paper proposes a Cascading model based on the character vectorization, This method uses BERT to represent the ambiguity of the word. In the agile BIGRU model, the initial entity recall is completed to increase the entity recall rate. Then the output of this layer is input into the high-level bilstm model of self attention mechanism to further filter and improve the accuracy of the final recognition. Finally, the CRF is used to complete the output of the final recognition result. Experiments show that the model can effectively improve the accuracy, accuracy and recall rate of complex Chinese entity recognition. The f1 value has increased by about 2% overall.

Chinese Named Entity Recognition for Clothing Knowledge Graph Construction

Clothing knowledge graph is a kind of vertical domain knowledge base constructed for the description of clothing knowledge in the field of textile and apparel. In this paper, based on the limitations of the clothing knowledge graph in the effect of entity extraction, the deep learning model and the statistical model are combined. A Chinese named entity recognition method based on CNN-BiLSTM-CRF is proposed. Firstly, the convolutional neural network(CNN) is used to extract the text features, and the character-level vectors with morphological features of the words are trained. Then the bi-directional long short term memory networks(LSTM) is used to learn the context features, and the vector representation of the context of each word is output. Finally, the conditional random fields(CRF) model is used for self-learning. Get the best tag sequence for the sentence. The method can automatically recognize the text, and does not rely on the artificial feature to obtain the semantic category information. Finally, the experimental data and evaluation methods are introduced. The experimental results show that the Chinese named entity recognition method based on CNN-BiLSTM-CRF is superior to other models in all indicators, indicating the effectiveness of the method.

Research on Chinese Named Entity Recognition Based on Ontology

As a critical role in many Natural Language Processing (NLP) applications, such as Information Extraction, Machine Translation etc, Chinese Named Entity Recognition (NER) remains a challenging task because of its characteristics. This paper proposes a method of Chinese NER, which combining Conditional Random Fields (CRFs) model with domain ontology as a semantic feature besides word and part of speech features. Experiments were made to compare the two kinds of feature templates, and the precision rate and recall rate of Chinese NER rose to 90.86% and 88.23%, which showed remarkable performance of the proposed approach. Combination of ontology and CRFs method increased effectively the precision and recall of Chinese NER.