Chinese Named Entity Recognition Research Articles

A Chinese named entity recognition method for landslide geological disasters based on deep learning

Landslide Named Entity Recognition (LNER) involves extracting specific entities from Chinese unstructured landslide disaster texts, which is crucial for constructing a knowledge graph and supporting landslide prevention efforts. This study proposes a deep learning-based LNER model that utilizes Bidirectional Encoder Representations from Transformer (BERT) for word embeddings and integrates the Conditional Random Fields (CRF) algorithm and projected gradient descent (PGD) adversarial neural networks to enhance sequence labeling accuracy. The practical implications of this research lie in improving the efficiency and precision of disaster information extraction, aiding in real-time decision-making and risk mitigation strategies. Experiments on the constructed dataset show that the model effectively identifies eight types of landslide entities, achieving a highest F1 score of 89.7%.

A Unified Model for Chinese Cyber Threat Intelligence Flat Entity and Nested Entity Recognition

In recent years, as cybersecurity threats have become increasingly severe and cyberattacks have occurred frequently, higher requirements have been put forward for cybersecurity protection. Therefore, the Named Entity Recognition (NER) technique, which is the cornerstone of Cyber Threat Intelligence (CTI) analysis, is particularly important. However, most existing NER studies are limited to recognizing single-layer flat entities, ignoring the possible nested entities in CTI. On the other hand, most of the existing studies focus on English CTIs, and the existing models performed poorly in a limited number of Chinese CTI studies. Given the above challenges, we propose in this paper a novel unified model, RBTG, which aims to identify flat and nested entities in Chinese CTI effectively. To overcome the difficult boundary recognition problem and the direction-dependent and distance-dependent properties in Chinese CTI NER, we use Global Pointer as the decoder and TENER as the encoder layer, respectively. Specifically, the Global Pointer layer solves the problem of the insensitivity of general NER methods to entity boundaries by utilizing the relative position information and the multiplicative attention mechanism. The TENER layer adapts to the Chinese CTI NER task by introducing an attention mechanism with direction awareness and distance awareness. Meanwhile, to cope with the complex feature capture of hierarchical structure and dependencies among Chinese CTI nested entities, the TENER layer solves the problem by following the structure of multiple self-attention layers and feed-forward network layers superimposed on each other in the Transformer. In addition, to fill the gap in the Chinese CTI nested entity dataset, we further apply the Large Language Modeling (LLM) technique and domain knowledge to construct a high-quality Chinese CTI nested entity dataset, CDTinee, which consists of six entity types selected from STIX, including nearly 4000 entity types extracted from more than 3000 threatening sentences. In the experimental session, we conduct extensive experiments on multiple datasets, and the results show that the proposed model RBTG outperforms the baseline model in both flat NER and nested NER.

Sequential lexicon enhanced bidirectional encoder representations from transformers: Chinese named entity recognition using sequential lexicon enhanced BERT

Lexicon Enhanced Bidirectional Encoder Representations from Transformers (LEBERT) has achieved great success in Chinese Named Entity Recognition (NER). LEBERT performs lexical enhancement with a Lexicon Adapter layer, which facilitates deep lexicon knowledge fusion at the lower layers of BERT. However, this method is likely to introduce noise words and does not consider the possible conflicts between words when fusing lexicon information. To address this issue, we advocate for a novel lexical enhancement method, Sequential Lexicon Enhanced BERT (SLEBERT) for the Chinese NER, which builds sequential lexicon to reduce noise words and resolve the problem of lexical conflict. Compared with LEBERT, it leverages the position encoding of sequential lexicon and adaptive attention mechanism of sequential lexicon to enhance the lexicon feature. Experiments on the four available datasets identified that SLEBERT outperforms other lexical enhancement models in performance and efficiency.

Chinese Named Entity Recognition Based on Multi-Level Representation Learning

Named Entity Recognition (NER) is a crucial component of Natural Language Processing (NLP). When dealing with the high diversity and complexity of the Chinese language, existing Chinese NER models face challenges in addressing word sense ambiguity, capturing long-range dependencies, and maintaining robustness, which hinders the accuracy of entity recognition. To this end, a Chinese NER model based on multi-level representation learning is proposed. The model leverages a pre-trained word-based embedding to capture contextual information. A linear layer adjusts dimensions to fit an Extended Long Short-Term Memory (XLSTM) network, enabling the capture of long-range dependencies and contextual information, and providing deeper representations. An adaptive multi-head attention mechanism is proposed to enhance the ability to capture global dependencies and comprehend deep semantic context. Additionally, GlobalPointer with rotational position encoding integrates global information for entity category prediction. Projected Gradient Descent (PGD) is incorporated, introducing perturbations in the embedding layer of the pre-trained model to enhance stability in noisy environments. The proposed model achieves F1-scores of 96.89%, 74.89%, 72.19%, and 80.96% on the Resume, Weibo, CMeEE, and CLUENER2020 datasets, respectively, demonstrating improvements over baseline and comparison models.

FE-CFNER: Feature Enhancement-based approach for Chinese Few-shot Named Entity Recognition

Although significant progress has been made in Chinese Named Entity Recognition (NER) methods based on deep learning, their performance often falls short in few-shot scenarios. Feature enhancement is considered a promising approach to address the issue of Chinese few-shot NER. However, traditional feature fusion methods tend to lead to the loss of important information and the integration of irrelevant information. Despite the benefits of incorporating BERT for improving entity recognition, its performance is limited when training data is insufficient. To tackle these challenges, this paper proposes a Feature Enhancement-based approach for Chinese Few-shot NER called FE-CFNER. FE-CFNER designs a double cross neural network to minimize information loss through the interaction of feature cross twice. Additionally, adaptive weights and a top-k mechanism are introduced to sparsify attention distributions, enabling the model to prioritize important information related to entities while excluding irrelevant information. To further enhance the quality of BERT embeddings, FE-CFNER employs a contrastive template for contrastive learning pre-training of BERT, enhancing BERT’s semantic understanding capability. We evaluate the proposed method on four sampled Chinese NER datasets: Weibo, Resume, Taobao, and Youku. Experimental results validate the effectiveness and superiority of FE-CFNER in Chinese few-shot NER tasks.

Chinese named entity recognition with multi-network fusion of multi-scale lexical information

Chinese named entity recognition with multi-network fusion of multi-scale lexical information

Chinese Named Entity Recognition based on adaptive lexical weights

Currently, many researchers use weights to merge self-matched words obtained through dictionary matching in order to enhance the performance of Named Entity Recognition (NER). However, these studies overlook the relationship between words and sentences when calculating lexical weights, resulting in fused word information that often does not align with the intended meaning of the sentence. Addressing above issue and enhance the prediction performance, we propose an adaptive lexical weight approach for determining lexical weights. Given a sentence, we utilize an enhanced global attention mechanism to compute the correlation between self-matching words and sentences, thereby focusing attention on crucial words while disregarding unreliable portions. Experimental results demonstrate that our proposed model outperforms existing state-of-the-art methods for Chinese NER of MRSA, Weibo, and Resume datasets.

KCB-FLAT: Enhancing Chinese Named Entity Recognition with Syntactic Information and Boundary Smoothing Techniques

Named entity recognition (NER) is a fundamental task in Natural Language Processing (NLP). During the training process, NER models suffer from over-confidence, and especially for the Chinese NER task, it involves word segmentation and introduces erroneous entity boundary segmentation, exacerbating over-confidence and reducing the model’s overall performance. These issues limit further enhancement of NER models. To tackle these problems, we proposes a new model named KCB-FLAT, designed to enhance Chinese NER performance by integrating enriched semantic information with the word-Boundary Smoothing technique. Particularly, we first extract various types of syntactic data and utilize a network named Key-Value Memory Network, based on syntactic information to functionalize this, integrating it through an attention mechanism to generate syntactic feature embeddings for Chinese characters. Subsequently, we employed an encoder named Cross-Transformer to thoroughly combine syntactic and lexical information to address the entity boundary segmentation errors caused by lexical information. Finally, we introduce a Boundary Smoothing module, combined with a regularity-conscious function, to capture the internal regularity of per entity, reducing the model’s overconfidence in entity probabilities through smoothing. Experimental results demonstrate that the proposed model achieves exceptional performance on the MSRA, Resume, Weibo, and self-built ZJ datasets, as verified by the F1 score.

The interactive fusion of characters and lexical information for Chinese named entity recognition

Many studies have demonstrated that incorporating lexical information into characters can effectively improve the performance of Chinese Named Entity Recognition (CNER). However, we argue that previous studies have not extensively explored the interactive relationship between characters and lexical information, and have only used the lexical information to enhance character-level representation. To address this limitation, we propose an interactive fusion approach that integrates characters and lexical information for CNER. Specifically, we first design graph attention networks to initially fuse character and lexical information within an interactive graph structure. Additionally, by introducing methods such as feedforward neural networks, residual connections, and layer normalization, the fusion effect of the graph attention network is further enhanced. Finally, concatenating and reducing dimensionality of character feature vectors and lexical feature vectors to achieve secondary fusion, thereby obtaining a more comprehensive feature representation. Experimental results on multiple datasets demonstrate that our proposed model outperforms other models that fuse lexical information. Particularly, on the CCKS2020 and Ontonotes datasets, our model achieves higher F1 scores than previous state-of-the-art models. The code is available via the link: https://github.com/wangye0523/The-interactive-fusion-of-characters-and-lexical-information-for-Chinese-named-entity-recognition.

A New Chinese Named Entity Recognition Method for Pig Disease Domain Based on Lexicon-Enhanced BERT and Contrastive Learning

A New Chinese Named Entity Recognition Method for Pig Disease Domain Based on Lexicon-Enhanced BERT and Contrastive Learning

A New Adapter Tuning of Large Language Model for Chinese Medical Named Entity Recognition

ABSTRACT Named entity recognition (NER) is a crucial step in extracting medical information from Chinese text, and fine-tuning large language models (LLMs) for this task is an effective approach. However, full parameter fine-tuning can potentially damage the model’s original parameters, resulting in catastrophic forgetting. To overcome this challenge, we introduce a novel adapter-based fine-tuning approach. Our adapter is integrated into the first and last transformers of the LLM, operating in parallel to the feed-forward network (FFN), following multi-head attention. It mirrors the FFN’s structure and uses the FFN’s weights for initializing. Additionally, to further enhance performance, we incorporate prefix embeddings into the first and last transformers. Our experiments on the Chinese medical NER benchmark demonstrate that our adapter, combined with prefix embeddings, achieves the highest F1-score of 65.90%, surpassing prompt templates (21.99%), in-context learning (18.65%), P-tuning (63.03%), and the benchmark for the Chinese medical NER task (62.40%). These results indicate that our adapter effectively fine-tunes the LLM for Chinese medical NER while preserving the original parameters.

An improved data augmentation approach and its application in medical named entity recognition

Performing data augmentation in medical named entity recognition (NER) is crucial due to the unique challenges posed by this field. Medical data is characterized by high acquisition costs, specialized terminology, imbalanced distributions, and limited training resources. These factors make achieving high performance in medical NER particularly difficult. Data augmentation methods help to mitigate these issues by generating additional training samples, thus balancing data distribution, enriching the training dataset, and improving model generalization. This paper proposes two data augmentation methods—Contextual Random Replacement based on Word2Vec Augmentation (CRR) and Targeted Entity Random Replacement Augmentation (TER)—aimed at addressing the scarcity and imbalance of data in the medical domain. When combined with a deep learning-based Chinese NER model, these methods can significantly enhance performance and recognition accuracy under limited resources. Experimental results demonstrate that both augmentation methods effectively improve the recognition capability of medical named entities. Specifically, the BERT-BiLSTM-CRF model achieved the highest F1 score of 83.587%, representing a 1.49% increase over the baseline model. This validates the importance and effectiveness of data augmentation in medical NER.

Semantic-enhanced graph neural network for named entity recognition in ancient Chinese books

Named entity recognition (NER) plays a crucial role in the extraction and utilization of knowledge of ancient Chinese books. However, the challenges of ancient Chinese NER not only originate from linguistic features such as the use of single characters and short sentences but are also exacerbated by the scarcity of training data. These factors together limit the capability of deep learning models, like BERT-CRF, in capturing the semantic representation of ancient Chinese characters. In this paper, we explore the semantic enhancement of NER in ancient Chinese books through the utilization of external knowledge. We propose a novel model based on Graph Neural Networks that integrates two different forms of external knowledge: dictionary-level and chapter-level information. Through the Graph Attention Mechanism (GAT), these external knowledge are effectively incorporated into the model’s input context. Our model is evaluated on the C_CLUE dataset, showing an improvement of 3.82% over the baseline BAC-CRF model. It also achieves the best score compared to several state-of-the-art dictionary-augmented models.

ELCA: Enhanced boundary location for Chinese named entity recognition via contextual association

ELCA: Enhanced boundary location for Chinese named entity recognition via contextual association

Improve the efficiency and accuracy of ophthalmologists’ clinical decision-making based on AI technology

BackgroundAs global aging intensifies, the prevalence of ocular fundus diseases continues to rise. In China, the tense doctor-patient ratio poses numerous challenges for the early diagnosis and treatment of ocular fundus diseases. To reduce the high risk of missed or misdiagnosed cases, avoid irreversible visual impairment for patients, and ensure good visual prognosis for patients with ocular fundus diseases, it is particularly important to enhance the growth and diagnostic capabilities of junior doctors. This study aims to leverage the value of electronic medical record data to developing a diagnostic intelligent decision support platform. This platform aims to assist junior doctors in diagnosing ocular fundus diseases quickly and accurately, expedite their professional growth, and prevent delays in patient treatment. An empirical evaluation will assess the platform’s effectiveness in enhancing doctors’ diagnostic efficiency and accuracy.MethodsIn this study, eight Chinese Named Entity Recognition (NER) models were compared, and the SoftLexicon-Glove-Word2vec model, achieving a high F1 score of 93.02%, was selected as the optimal recognition tool. This model was then used to extract key information from electronic medical records (EMRs) and generate feature variables based on diagnostic rule templates. Subsequently, an XGBoost algorithm was employed to construct an intelligent decision support platform for diagnosing ocular fundus diseases. The effectiveness of the platform in improving diagnostic efficiency and accuracy was evaluated through a controlled experiment comparing experienced and junior doctors.ResultsThe use of the diagnostic intelligent decision support platform resulted in significant improvements in both diagnostic efficiency and accuracy for both experienced and junior doctors (P < 0.05). Notably, the gap in diagnostic speed and precision between junior doctors and experienced doctors narrowed considerably when the platform was used. Although the platform also provided some benefits to experienced doctors, the improvement was less pronounced compared to junior doctors.ConclusionThe diagnostic intelligent decision support platform established in this study, based on the XGBoost algorithm and NER, effectively enhances the diagnostic efficiency and accuracy of junior doctors in ocular fundus diseases. This has significant implications for optimizing clinical diagnosis and treatment.

Improved XLNet modeling for Chinese named entity recognition of edible fungus.

The diversity of edible fungus species and the extent of mycological knowledge pose significant challenges to the research, cultivation, and popularization of edible fungus. To tackle this challenge, there is an urgent need for a rapid and accurate method of acquiring relevant information. The emergence of question and answer (Q&A) systems has the potential to solve this problem. Named entity recognition (NER) provides the basis for building an intelligent Q&A system for edible fungus. In the field of edible fungus, there is a lack of a publicly available Chinese corpus suitable for use in NER, and conventional methods struggle to capture long-distance dependencies in the NER process. This paper describes the establishment of a Chinese corpus in the field of edible fungus and introduces an NER method for edible fungus information based on XLNet and conditional random fields (CRFs). Our approach combines an iterated dilated convolutional neural network (IDCNN) with a CRF. First, leveraging the XLNet model as the foundation, an IDCNN layer is introduced. This layer addresses the limited capacity to capture features across utterances by extending the receptive field of the convolutional kernel. The output of the IDCNN layer is input to the CRF layer, which mitigates any labeling logic errors, resulting in the globally optimal labels for the NER task relating to edible fungus. Experimental results show that the precision achieved by the proposed model reaches 0.971, with a recall of 0.986 and an F1-score of 0.979. The proposed model outperforms existing approaches in terms of these evaluation metrics, effectively recognizing entities related to edible fungus information and offering methodological support for the construction of knowledge graphs.

LB-BMBC: MHBiaffine-CNN to Capture Span Scores with BERT Injected with Lexical Information for Chinese NER

A substantial body of research has shown that introducing lexical information in Chinese Named Entity Recognition (NER) tasks can enhance the semantic and boundary information of Chinese words. However, in most methods, the introduction of lexical information occurs at the model architecture level, which cannot fully leverage the lexicon learning capability of pre-trained models. Therefore, we propose seamless integration of external Lexicon knowledge into the Transformer layer of BERT. Additionally, we have observed that in span-based recognition, adjacent spans have special spatial relationships. To capture this relationship, we extend the work after Biaffine and use Convolutional Neural Networks (CNN) to treat the score matrix as an image, allowing us to interact with the spatial relationships of spans. Our proposed LB-BMBC model was experimented on four publicly available Chinese NER datasets: Resume, Weibo, OntoNotes v4, and MSRA. In particular, during ablation experiments, we found that CNN can significantly improve performance.

Chinese Named Entity Recognition method based on multi-feature fusion and biaffine

Chinese Named Entity Recognition (CNER) focuses on precisely identifying predefined structural categories in unstructured Chinese text. Most existing CNER models do not consider the unique glyph and pinyin features of Chinese characters, but the rich semantic features hidden behind these features have a good effect on enhancing the judgment ability of language models. At the same time, it is difficult to identify the boundaries of Chinese nested entities, and accurately identifying the boundaries of entities within nested entities is also a difficult problem to solve. We propose a CNER method based on multi-feature fusion technology and biaffine mechanism to address the above issues: In the input representation layer, integrate the glyph and pinyin features of Chinese characters together, intuitively capturing the semantics of Chinese characters. Furthermore, biaffine mechanism has been introduced to provide a comprehensive view of the input on a global scale. This mechanism effectively converts the task of entity recognition into a problem of assigning scores to spans, hence enhancing the precision of identifying entity borders. In order to evaluate the efficacy of the model, a series of experiments were done on three Chinese entity recognition datasets: Resume, MSRA, and People Daily. The experimental results show that the solid boundary can be identified more accurately, and the F1 values of 96.49%, 96.26% and 96.19% are obtained respectively, which has a better recognition effect than the baseline model.

GlyReShot: A glyph-aware model with label refinement for few-shot Chinese agricultural named entity recognition

Chinese agricultural named entity recognition (NER) has been studied with supervised learning for many years. However, considering the scarcity of public datasets in the agricultural domain, exploring this task in the few-shot scenario is more practical for real-world demands. In this paper, we propose a novel model named GlyReShot, integrating the knowledge of Chinese character glyph into few-shot NER models. Although the utilization of glyph has been proven successful in supervised models, two challenges still persist in the few-shot setting, i.e., how to obtain glyph representations and when to integrate them into the few-shot model. GlyReShot handles the two challenges by introducing a lightweight glyph representation obtaining module and a training-free label refinement strategy. Specifically, the glyph representations are generated based on the descriptive sentences by filling the predefined template. As most steps come before training, this module aligns well with the few-shot setting. Furthermore, by computing the confidence values for draft predictions, the refinement strategy selectively utilizes the glyph information only when the confidence values are relatively low, thus mitigating the influence of noise. Finally, we annotate a new agricultural NER dataset and the experimental results demonstrate effectiveness of GlyReShot for few-shot Chinese agricultural NER.

SENCR: A Span Enhanced Two-Stage Network with Counterfactual Rethinking for Chinese NER

Recently, lots of works that incorporate external lexicon information into character-level Chinese named entity recognition(NER) to overcome the lackness of natural delimiters of words, have achieved many advanced performance. However, obtaining and maintaining high-quality lexicons is costly, especially in special domains. In addition, the entity boundary bias caused by high mention coverage in some boundary characters poses a significant challenge to the generalization of NER models but receives little attention in the existing literature. To address these issues, we propose SENCR, a Span Enhanced Two-stage Network with Counterfactual Rethinking for Chinese NER, that contains a boundary detector for boundary supervision, a convolution-based type classifier for better span representation and a counterfactual rethinking(CR) strategy for debiased boundary detection in inference. The proposed boundary detector and type classifier are jointly trained with the same contextual encoder and then the trained boundary detector is debiased by our proposed CR strategy without modifying any model parameters in the inference stage. Extensive experiments on four Chinese NER datasets show the effectiveness of our proposed approach.