Yumei Hao^1,2, Haiyan Wang^1,2,*, Dong Zhang³

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2989-3005, 2025, DOI:10.32604/cmc.2025.063008 - 16 April 2025

Abstract Named entity recognition (NER) in musk deer domain is the extraction of specific types of entities from unstructured texts, constituting a fundamental component of the knowledge graph, Q&A system, and text summarization system of musk deer domain. Due to limited annotated data, diverse entity types, and the ambiguity of Chinese word boundaries in musk deer domain NER, we present a novel NER model, CAELF-GP, which is based on cross-attention mechanism enhanced lexical features (CAELF). Specifically, we employ BERT as a character encoder and advocate the integration of external lexical information at the character representation layer.… More >

Jiahao Wu^1,2, Jinzhong Xu¹, Xiaoming Liu^1,*, Guan Yang^1,3, Jie Liu⁴

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2809-2828, 2025, DOI:10.32604/cmc.2025.061359 - 16 April 2025

Abstract Large language models cross-domain named entity recognition task in the face of the scarcity of large language labeled data in a specific domain, due to the entity bias arising from the variation of entity information between different domains, which makes large language models prone to spurious correlations problems when dealing with specific domains and entities. In order to solve this problem, this paper proposes a cross-domain named entity recognition method based on causal graph structure enhancement, which captures the cross-domain invariant causal structural representations between feature representations of text sequences and annotation sequences by establishing… More >

Rui Fang, Liangzhong Cui^*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3247-3275, 2025, DOI:10.32604/cmc.2025.060422 - 16 April 2025

Abstract Named Entity Recognition (NER) is vital in natural language processing for the analysis of news texts, as it accurately identifies entities such as locations, persons, and organizations, which is crucial for applications like news summarization and event tracking. However, NER in the news domain faces challenges due to insufficient annotated data, complex entity structures, and strong context dependencies. To address these issues, we propose a new Chinese-named entity recognition method that integrates transfer learning with word embeddings. Our approach leverages the ERNIE pre-trained model for transfer learning and obtaining general language representations and incorporates the More >

Chenglong Mi¹, Huaibin Qin^1,*, Quan Qi¹, Pengxiang Zuo²

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3773-3796, 2025, DOI:10.32604/cmc.2024.059455 - 06 March 2025

Abstract In recent years, with the rapid development of deep learning technology, relational triplet extraction techniques have also achieved groundbreaking progress. Traditional pipeline models have certain limitations due to error propagation. To overcome the limitations of traditional pipeline models, recent research has focused on jointly modeling the two key subtasks-named entity recognition and relation extraction-within a unified framework. To support future research, this paper provides a comprehensive review of recently published studies in the field of relational triplet extraction. The review examines commonly used public datasets for relational triplet extraction techniques and systematically reviews current mainstream… More >

Qiuyu Lai^1,2, Wang Kang³, Lei Yang^1,2, Chun Yang^1,2,*, Delin Zhang^2,*

Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 649-664, 2024, DOI:10.32604/iasc.2024.051724 - 06 September 2024

Abstract Mathematical named entity recognition (MNER) is one of the fundamental tasks in the analysis of mathematical texts. To solve the existing problems of the current neural network that has local instability, fuzzy entity boundary, and long-distance dependence between entities in Chinese mathematical entity recognition task, we propose a series of optimization processing methods and constructed an Adversarial Training and Bidirectional long short-term memory-Selfattention Conditional random field (AT-BSAC) model. In our model, the mathematical text was vectorized by the word embedding technique, and small perturbations were added to the word vector to generate adversarial samples, while More >

Meijing Li^*, Runqing Huang, Xianxian Qi

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2283-2299, 2024, DOI:10.32604/cmc.2024.053630 - 15 August 2024

Abstract Chinese Clinical Named Entity Recognition (CNER) is a crucial step in extracting medical information and is of great significance in promoting medical informatization. However, CNER poses challenges due to the specificity of clinical terminology, the complexity of Chinese text semantics, and the uncertainty of Chinese entity boundaries. To address these issues, we propose an improved CNER model, which is based on multi-feature fusion and multi-scale local context enhancement. The model simultaneously fuses multi-feature representations of pinyin, radical, Part of Speech (POS), word boundary with BERT deep contextual representations to enhance the semantic representation of text… More >

Yan Xiang^1,2, Xuedong Zhao^1,2, Junjun Guo^1,2,*, Zhiliang Shi³, Enbang Chen³, Xiaobo Zhang³

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4149-4167, 2024, DOI:10.32604/cmc.2024.050229 - 20 June 2024

Abstract Chinese named entity recognition (CNER) has received widespread attention as an important task of Chinese information extraction. Most previous research has focused on individually studying flat CNER, overlapped CNER, or discontinuous CNER. However, a unified CNER is often needed in real-world scenarios. Recent studies have shown that grid tagging-based methods based on character-pair relationship classification hold great potential for achieving unified NER. Nevertheless, how to enrich Chinese character-pair grid representations and capture deeper dependencies between character pairs to improve entity recognition performance remains an unresolved challenge. In this study, we enhance the character-pair grid representation… More >

Xiaohui Cui^1,2,#, Chao Song^1,2,#, Dongmei Li^1,2,*, Xiaolong Qu^1,2, Jiao Long^1,2, Yu Yang^1,2, Hanchao Zhang³

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3603-3618, 2024, DOI:10.32604/cmc.2024.047321 - 26 March 2024

Abstract Named Entity Recognition (NER) stands as a fundamental task within the field of biomedical text mining, aiming to extract specific types of entities such as genes, proteins, and diseases from complex biomedical texts and categorize them into predefined entity types. This process can provide basic support for the automatic construction of knowledge bases. In contrast to general texts, biomedical texts frequently contain numerous nested entities and local dependencies among these entities, presenting significant challenges to prevailing NER models. To address these issues, we propose a novel Chinese nested biomedical NER model based on RoBERTa and Global Pointer… More >

Jing Yang, Bin Ji, Shasha Li^*, Jun Ma, Jie Yu

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4303-4315, 2024, DOI:10.32604/cmc.2023.035594 - 26 March 2024

Abstract Named entity recognition (NER) is a fundamental task of information extraction (IE), and it has attracted considerable research attention in recent years. The abundant annotated English NER datasets have significantly promoted the NER research in the English field. By contrast, much fewer efforts are made to the Chinese NER research, especially in the scientific domain, due to the scarcity of Chinese NER datasets. To alleviate this problem, we present a Chinese scientific NER dataset–SciCN, which contains entity annotations of titles and abstracts derived from 3,500 scientific papers. We manually annotate a total of 62,059 entities,… More >

Zhen Zhen¹, Jian Gao^1,2,*

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 299-323, 2023, DOI:10.32604/cmc.2023.042090 - 31 October 2023

Abstract In recent years, cyber attacks have been intensifying and causing great harm to individuals, companies, and countries. The mining of cyber threat intelligence (CTI) can facilitate intelligence integration and serve well in combating cyber attacks. Named Entity Recognition (NER), as a crucial component of text mining, can structure complex CTI text and aid cybersecurity professionals in effectively countering threats. However, current CTI NER research has mainly focused on studying English CTI. In the limited studies conducted on Chinese text, existing models have shown poor performance. To fully utilize the power of Chinese pre-trained language models… More >