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 >

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 >

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 >

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 >