Chinese Named Entity Recognition Research Articles

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.

Three Heads Are Better than One: Improving Cross-Domain NER with Progressive Decomposed Network

Cross-domain named entity recognition (NER) tasks encourage NER models to transfer knowledge from data-rich source domains to sparsely labeled target domains. Previous works adopt the paradigms of pre-training on the source domain followed by fine-tuning on the target domain. However, these works ignore that general labeled NER source domain data can be easily retrieved in the real world, and soliciting more source domains could bring more benefits. Unfortunately, previous paradigms cannot efficiently transfer knowledge from multiple source domains. In this work, to transfer multiple source domains' knowledge, we decouple the NER task into the pipeline tasks of mention detection and entity typing, where the mention detection unifies the training object across domains, thus providing the entity typing with higher-quality entity mentions. Additionally, we request multiple general source domain models to suggest the potential named entities for sentences in the target domain explicitly, and transfer their knowledge to the target domain models through the knowledge progressive networks implicitly. Furthermore, we propose two methods to analyze in which source domain knowledge transfer occurs, thus helping us judge which source domain brings the greatest benefit. In our experiment, we develop a Chinese cross-domain NER dataset. Our model improved the F1 score by an average of 12.50% across 8 Chinese and English datasets compared to models without source domain data.

Enhanced Chinese named entity recognition with multi-granularity BERT adapter and efficient global pointer

AbstractNamed Entity Recognition (NER) plays a crucial role in the field of Natural Language Processing, holding significant value in applications such as information extraction, knowledge graphs, and question–answering systems. However, Chinese NER faces challenges such as semantic complexity, uncertain entity boundaries, and nested structures. To address these issues, this study proposes an innovative approach, namely Multi-Granularity BERT Adapter and Efficient Global Pointer (MGBERT-Pointer). The semantic encoding layer adopts Multi-Granularity Adapter (MGA), while the decoding layer employs Efficient Global Pointer (EGP) network, ensuring collaborative functionality. The MGA, incorporating Character Adapter, Entity Adapter, and Lexicon Adapter through interactive mechanisms, are deeply integrated into the BERT base, significantly enhancing the model’s ability to handle complex contexts and ambiguities. The EGP, utilizing Rotary Position Embedding, resolves the issue of insufficient boundary information in traditional attention mechanisms, thereby improving the model’s understanding and recognition of nested entity structures. Experimental results on four public datasets demonstrate a significant enhancement in Chinese NER performance achieved by the MGBERT-Pointer model.

A Chinese named entity recognition model incorporating recurrent cell and information state recursion

Chinese is characterized by high syntactic complexity, chaotic annotation granularity, and slow convergence. Joint learning models can effectively improve the accuracy of Chinese Named Entity Recognition (NER), but they focus too much on local feature information and reduce the ability of long sequence feature extraction. To address the limitations of long sequence feature extraction ability, we propose a Chinese NER model called Incorporating Recurrent Cell and Information State Recursion (IRCSR-NER). The model integrates recurrent cells and information state recursion to improve the recognition ability of long entity boundaries. To solve the problem that Chinese and English have different focuses in syntactic analysis. We use the syntactic dependency approach to add lexical relationship information to sentences represented at the word level. The IRCSR-NER is applied to sequence feature extraction to improve the model efficiency and long-text feature extraction ability. The model captures contextual long-distance dependent information while focusing on local feature information. We evaluated our proposed model using four public datasets and compared it with other mainstream models. Experimental results demonstrate that our model outperforms traditional and mainstream models.

ATBBC: Named entity recognition in emergency domains based on joint BERT-BILSTM-CRF adversarial training

With the rapid growth of Internet penetration, identifying emergency information from network news has become increasingly significant for emergency monitoring and early warning. Although deep learning models have been commonly used in Chinese Named Entity Recognition (NER), they require a significant amount of well-labeled training data, which is difficult to obtain for emergencies. In this paper, we propose an NER model that combines bidirectional encoder representations from Transformers (BERT), bidirectional long-short-term memory (BILSTM), and conditional random field (CRF) based on adversarial training (ATBBC) to address this issue. Firstly, we constructed an emergency dataset (ED) based on the classification and coding specifications of the national emergency platform system. Secondly, we utilized the BERT pre-training model with adversarial training to extract text features. Finally, BILSTM and CRF were used to predict the probability distribution of entity labels and decode the probability distribution into corresponding entity labels.Experiments on the ED show that our model achieves an F1-score of 85.39% on the test dataset, which proves the effectiveness of our model.

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 template augmented distant supervision framework for Chinese named entity recognition

Distant supervision has been proven to be an efficient way of generating labeled instances for Named Entity Recognition (NER). However, it suffers from dictionary biases and ambiguous entities, resulting in noisy and incomplete labels. To overcome this drawback, this paper proposes a template augmented distant supervision framework, which generates high-quality labeled training data with minimal human effort. Specifically, we use distant supervision to extract sentences that contain entities and apply a pre-trained language model to encode these sentences. The encoded sentences are clustered and then for each cluster, three sentences are sampled out to form a seed template pool. The seed templates are calibrated and decomposed to decouple the connection between different parts. Finally, the seed templates and entity dictionary are combined with pre-trained language model to generate semantically coherent and precisely labeled training data. Experimental results on the EC and NEWS datasets and a practical electronic after-sale Q&A dataset with multiple pre-trained language models demonstrate that the proposed framework is able to improve the F1 score of the distantly supervised NER models by 7.9%–12.9%.

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

PGD-GP: A Chinese Named Entity Recognition Model for Constructing Food Safety Standard Knowledge Graph

PGD-GP: A Chinese Named Entity Recognition Model for Constructing Food Safety Standard Knowledge Graph

Local or global? A novel transformer for Chinese named entity recognition based on multi-view and sliding attention

Local or global? A novel transformer for Chinese named entity recognition based on multi-view and sliding attention

Chinese Named Entity Recognition Based on Boundary Enhancement with Multi-Class Information

Compared with English named entity recognition (NER), Chinese NER faces significant challenges due to the flexible, non-standard word formation and vague word boundaries, which cause a lot of boundary ambiguity and reduce the accuracy of entity identification. To address this issue, we propose a boundary enhancement with multi-class information model (BEMCI). The model integrates multiple types of information into text embedding while enhancing the subsequent syntax-structure information. A syntactic information analysis module is designed to highlight important syntax information from three aspects, namely part-of-speech tags, syntactic constituents, and dependency relations, to analyze sentence structures. Meanwhile, an improved contextual attention mechanism, which combines contextual and syntactic information using a gate mechanism to control the weight fusion, is proposed to further enhance the model’s boundary determination. Multiple sets of experiments conducted on six general datasets show that BEMCI outperforms other baselines, achieving the best results in four of these six datasets.

EPIC: An epidemiological investigation of COVID-19 dataset for Chinese named entity recognition

Since the outbreak of COVID-19, it has had a huge impact on the whole world. In China, there have been a large number of epidemiological investigation reports in response to COVID-19. In order to more effectively analyze and utilize these reports for future large-scale epidemics, this paper proposes a new three-stage annotation method and utilizes the method to construct an epidemiological investigation of COVID-19 dataset for Chinese named entity recognition (CNER)—EPIC (EPidemiological Investigation of COVID-19). EPIC contains 10 categories of named entities, focusing on the travel history of confirmed cases. The corpus of the EPIC consists of 226 official epidemiological investigation reports and the inter-annotator agreement of the EPIC reaches 0.97. Based on EPIC, this paper proposes the Semantic Embedding with Chinese Character Structural Features (SECCSF) method to improve the accuracy of segmentation boundary detection and entity category determination in CNER. In the experimental phase, this paper implements several baselines to conduct experiments on EPIC. The baseline with the SECCSF method achieves an F1 value of 0.892. This indicates the effectiveness of the SECCSF method for the NER task on the EPIC. We release the EPIC at: https://github.com/tinyyhorm/EPIC.

MFF-CNER: A Multi-feature Fusion Model for Chinese Named Entity Recognition in Finance Securities

The objective of Chinese financial securities named entity recognition is to extract relevant entities from unstructured Chinese text, such as news, announcements, and research reports, that impact security prices. Recognizing entities in this field is challenging due to the abundance of specialized terms, diverse expressions, and the limited feature extraction capabilities of traditional models. To address this, we propose MFF-CNER, a multi-feature fusion model, to improve the effectiveness of Chinese financial securities named entity recognition. MFF-CNER encompasses several key steps. Firstly, it leverages a BERT pre-training model to capture semantic features at the character level. Secondly, a BiLSTM network is utilized to capture contextual features specific to financial securities text. Additionally, we introduce an Iterated Dilated Convolutional Neural Network (IDCNN) to blend, and extract local features, incorporating an Attention mechanism for weighted feature integration. Finally, the predicted sequences are optimized, and decoded using the Conditional Random Field (CRF). To validate the state-of-the-art performance of MFF-CNER in this domain, we compare it with five popular methods on a Chinese financial securities dataset annotated with the BIO labeling scheme. Notably, MFF-CNER demonstrates superior performance while maintaining compatibility among its components. Furthermore, we evaluate the applicability of MFF-CNER in the Chinese financial securities domain by utilizing public datasets from diverse domains, including social media (WEIBO), and news (MSRA). This research holds practical significance for downstream applications, such as constructing financial securities knowledge graphs, and analyzing factors that influence security prices.

Multi-Meta Information Embedding Enhanced BERT for Chinese Mechanics Entity Recognition

The automatic extraction of key entities in mechanics problems is an important means to automatically solve mechanics problems. Nevertheless, for standard Chinese, compared with the open domain, mechanics problems have a large number of specialized terms and composite entities, which leads to a low recognition capability. Although recent research demonstrates that external information and pre-trained language models can improve the performance of Chinese Named Entity Recognition (CNER), few efforts have been made to combine the two to explore high-performance algorithms for extracting mechanics entities. Therefore, this article proposes a Multi-Meta Information Embedding Enhanced Bidirectional Encoder Representation from Transformers (MMIEE-BERT) for recognizing entities in mechanics problems. The proposed method integrates lexical information and radical information into BERT layers directly by employing an information adapter layer (IAL). Firstly, according to the characteristics of Chinese, a Multi-Meta Information Embedding (MMIE) including character embedding, lexical embedding, and radical embedding is proposed to enhance Chinese sentence representation. Secondly, an information adapter layer (IAL) is proposed to fuse the above three embeddings into the lower layers of the BERT. Thirdly, a Bidirectional Long Short-Term Memory (BiLSTM) network and a Conditional Random Field (CRF) model are applied to semantically encode the output of MMIEE-BERT and obtain each character’s label. Finally, extensive experiments were carried out on the dataset built by our team and widely used datasets. The results demonstrate that the proposed method has more advantages than the existing models in the entity recognition of mechanics problems, and the precision, recall, and F1 score were improved. The proposed method is expected to provide an automatic means for extracting key information from mechanics problems.

CLART: A cascaded lattice-and-radical transformer network for Chinese medical named entity recognition

Chinese medical named entity recognition (NER) is a fundamental task in Chinese medical natural language processing, aiming to recognize Chinese medical entities within unstructured medical texts. However, it poses significant challenges mainly due to the extensive usage of medical terms in Chinese medical texts. Although previous studies have made attempts to incorporate lexical or radical knowledge in order to improve the comprehension of medical texts, these studies either focus solely on one of these aspects or utilize a basic concatenation operation to combine these features, which fails to fully utilize the potential of lexical and radical knowledge. In this paper, we propose a novel Cascaded LAttice-and-Radical Transformer (CLART) network to exploit both lexical and radical information for Chinese medical NER. Specifically, given a sentence, a medical lexicon, and a radical dictionary, we first construct a flat lattice (i.e., character-word sequence) for the sentence and radical components of each Chinese character through word matching and radical parsing, respectively. We then employ a lattice Transformer module to capture the dense interactions between characters and matched words, facilitating the enhanced utilization of lexical knowledge. Subsequently, we design a radical Transformer module to model the dense interactions between the lattice and radical features, facilitating better fusion of the lexical and radical knowledge. Finally, we feed the updated lattice-and-radical-aware character representations into a Conditional Random Fields (CRF) decoder to obtain the predicted labels. Experimental results conducted on two publicly available Chinese medical NER datasets show the effectiveness of the proposed method.

Chinese Named Entity Recognition in Football Based on ALBERT-BiLSTM Model

Football is one of the most popular sports in the world, arousing a wide range of research topics related to its off- and on-the-pitch performance. The extraction of football entities from football news helps to construct sports frameworks, integrate sports resources, and timely capture the dynamics of the sports through visual text mining results, including the connections among football players, football clubs, and football competitions, and it is of great convenience to observe and analyze the developmental tendencies of football. Therefore, in this paper, we constructed a 1000,000-word Chinese corpus in the field of football and proposed a BiLSTM-based model for named entity recognition. The ALBERT-BiLSTM combination model of deep learning is used for entity extraction of football textual data. Based on the BiLSTM model, we introduced ALBERT as a pre-training model to extract character and enhance the generalization ability of word embedding vectors. We then compared the results of two different annotation schemes, BIO and BIOE, and two deep learning models, ALBERT-BiLSTM-CRF and ALBERT BiLSTM. It was verified that the BIOE tagging was superior than BIO, and the ALBERT-BiLSTM model was more suitable for football datasets. The precision, recall, and F-Score of the model were 85.4%, 83.47%, and 84.37%, correspondingly.

Improving Low-Resource Chinese Named Entity Recognition Using Bidirectional Encoder Representation from Transformers and Lexicon Adapter

Due to their individual advantages, the integration of lexicon information and pre-trained models like BERT has been widely adopted in Chinese sequence labeling tasks. However, given their high demand for training data, efforts have been made to enhance their performance in low-resource scenarios. Currently, certain specialized domains, such as agriculture, the industrial sector, and the metallurgical industry, suffer from a scarcity of data. Consequently, there is a dearth of effective models for entity relationship recognition when faced with limited data availability. Inspired by this, we constructed a suitable small balanced dataset and proposed a based-domain-NER model. Firstly, we construct a domain-specific dictionary based on mine hoist equipment and fault text and generate a dictionary tree to obtain word vector information. Secondly, we use a Lexicon Adapter to obtain the vector information of the domain-specific dictionary feature words matched using the characters and calculate the weights between their word vectors, integrating position encoding to enhance the positional information of the word vectors. Finally, we incorporate word vector information into the feature extraction layer to enhance the boundary information of domain entities and mitigate the semantic loss problem caused via using only character feature representation. Experimental results on a manually annotated dataset of mine hoist fault texts show that this method outperforms BiLSTM, BiLSTM-CRF, BERT, BERT-BiLSTM-CRF, and LEBERT, effectively improving the accuracy of named entity recognition (NER) for mine hoist faults.

A novel Data and Model Centric artificial intelligence based approach in developing high-performance Named Entity Recognition for Bengali Language.

Named Entity Recognition (NER) plays a significant role in enhancing the performance of all types of domain specific applications in Natural Language Processing (NLP). According to the type of application, the goal of NER is to identify target entities based on the context of other existing entities in a sentence. Numerous architectures have demonstrated good performance for high-resource languages such as English and Chinese NER. However, currently existing NER models for Bengali could not achieve reliable accuracy due to morphological richness of Bengali and limited availability of resources. This work integrates both Data and Model Centric AI concepts to achieve a state-of-the-art performance. A unique dataset was created for this study demonstrating the impact of a good quality dataset on accuracy. We proposed a method for developing a high quality NER dataset for any language. We have used our dataset to evaluate the performance of various Deep Learning models. A hybrid model performed with the exact match F1 score of 87.50%, partial match F1 score of 92.31%, and micro F1 score of 98.32%. Our proposed model reduces the need for feature engineering and utilizes minimal resources.

MCA-NER: Multi-Contextualized Adversarial-Based Attentional Deep Neural Network for Named Entity Recognition

Multi-contextualized representations learning is vital for named entity recognition (NER), which is a fundamental task for effectively extracting structured information from unstructured text, and forming knowledge bases. This task is particularly challenging when dealing with Chinese text given the absence of evident word boundaries. Chinese word segmentation (CWS) can be leveraged to recognize word boundaries, but named entities often encompass multiple segmented words, making it crucial to use boundary information to correctly recognize and distinguish the relationships between these words. In this paper, we propose MCA-NER, a multi-contextualized adversarial-based attentional deep learning approach for Chinese NER, which combines CWS and part-of-speech (POS) tagging information with the classic BiLSTM-CRF NER model, using adversarial multi-task learning. The model incorporates several self-attention components for adversarial and multi-task learning, effectively synthesizing task-specific and common information attribution while improving performance across all three tasks. Experimental results on the three datasets provide compelling evidence that supports the effectiveness and performance of our model.