Chinese Named Entity Recognition: Applications and Challenges

Xi, Qisen; Ren, Yi; Yao, Siyu; Wu, Guohua; Miao, Gongxun; Zhang, Zhen

doi:10.1007/978-3-030-71590-8_4

Cited by 5 publications

(3 citation statements)

References 27 publications

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“…Compared to English NER, Chinese NER faces more challenges primarily due to the difficulty in determining entity boundaries in Chinese text and the complex syntactical structure of the Chinese language. Previous research [4] compared character-based and word-based approaches, and characterbased NER methods often fail to fully harness explicit word and word sequence information, despite its potential value. To leverage lexical features, Zhang et al [5] proposed the Lattice-LSTM model, which encodes all words matched by individual characters in a sentence into a DAG.…”

Section: Lexical Information For Nermentioning

confidence: 99%

“…CNN block [2,4] CNN dim [120,200] • Lattice-LSTM [5]: For the Chinese NER task, an LSTM model using a lattice structure, Lattice-LSTM, is proposed to encode both the character features of the input sequence and all potential words matched with the lexicon for NER after fusing the information of words and word sequences. • CAN-NER [52]: Extract local character information through CNN, and then capture adjacent character or context information using a global self-attention layer composed of GRU.…”

Section: Biaffine Size 400mentioning

confidence: 99%

“…This algorithm offers robust support for the execution of information extraction tasks and enables the implementation of subsequent downstream applications. Compared to English NER, Chinese NER [4] faces greater challenges. This is primarily due to the complexities of Chinese language involving word segmentation, difficulty in determining boundaries of related entities, and intricate grammatical structures.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Guo,

Zhang

2024

Int J Comput Intell Syst

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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.

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Section: Lexical Information For Nermentioning

confidence: 99%

Section: Biaffine Size 400mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Guo,

Zhang

2024

Int J Comput Intell Syst

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A Question and Answering System for Mental Health of the Elderly Based on BiLSTM-CRF Model and Knowledge Graph

He,

Lin,

Huang

et al. 2023

Lecture Notes in Computer Science

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Exploring Biomedical Named Entity Recognition via SciSpaCy and BioBERT Models

Jolly,

Pandey,

Singh

et al. 2024

TOBEJ

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Introduction Biological Named Entity Recognition (BioNER) is a crucial preprocessing step for Bio-AI analysis. Methods Our paper explores the field of Biomedical Named Entity Recognition (BioNER) by closely analysing two advanced models, SciSpaCy and BioBERT. We have made two distinct contributions: Initially, we thoroughly train these models using a wide range of biological datasets, allowing for a methodical assessment of their performance in many areas. We offer detailed evaluations using important parameters like F1 scores and processing speed to provide precise insights into the effectiveness of BioNER activities. Results Furthermore, our study provides significant recommendations for choosing tools that are customised to meet unique BioNER needs, thereby enhancing the efficiency of Named Entity Recognition in the field of biomedical research. Our work focuses on tackling the complex challenges involved in BioNER and enhancing our understanding of model performance. Conclusion The goal of this research is to drive progress in this important field and enable more effective use of advanced data analysis tools for extracting valuable insights from biomedical literature.

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Chinese Named Entity Recognition: Applications and Challenges

Cited by 5 publications

References 27 publications

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

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

A Question and Answering System for Mental Health of the Elderly Based on BiLSTM-CRF Model and Knowledge Graph

Exploring Biomedical Named Entity Recognition via SciSpaCy and BioBERT Models

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