Towards Chinese clinical named entity recognition by dynamic embedding using domain-specific knowledge

Li, Yuan; Du, Guodong; Xiang, Yan; Li, Shaozi; Ma, Lei; Shao, Dangguo; Wang, Xiongbin; Chen, Haoyu

doi:10.1016/j.jbi.2020.103435

Cited by 34 publications

(15 citation statements)

References 21 publications

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“…Settles et al [11] used simple orthogonal features in CRF to carry out pertinent biomedical entity recognition. Similar research has also been conducted in several other fields, such as chemical entities [12], E-mail [13], tourism domain entities [14], crime entities [15], and clinical entities [16].…”

Section: Introductionmentioning

confidence: 74%

Extracting Knowledge Entities from Sci-Tech Intelligence Resources Based on BiLSTM and Conditional Random Field

Liao

Huang

et al. 2021

IEICE Trans. Inf. & Syst.

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There are many knowledge entities in sci-tech intelligence resources. Extracting these knowledge entities is of great importance for building knowledge networks, exploring the relationship between knowledge, and optimizing search engines. Many existing methods, which are mainly based on rules and traditional machine learning, require significant human involvement, but still suffer from unsatisfactory extraction accuracy. This paper proposes a novel approach for knowledge entity extraction based on BiLSTM and conditional random field (CRF).A BiLSTM neural network to obtain the context information of sentences, and CRF is then employed to integrate global label information to achieve optimal labels. This approach does not require the manual construction of features, and outperforms conventional methods. In the experiments presented in this paper, the titles and abstracts of 20,000 items in the existing sci-tech literature are processed, of which 50,243 items are used to build benchmark datasets. Based on these datasets, comparative experiments are conducted to evaluate the effectiveness of the proposed approach. Knowledge entities are extracted and corresponding knowledge networks are established with a further elaboration on the correlation of two different types of knowledge entities. The proposed research has the potential to improve the quality of sci-tech information services.

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Section: Introductionmentioning

confidence: 74%

Extracting Knowledge Entities from Sci-Tech Intelligence Resources Based on BiLSTM and Conditional Random Field

Liao

Huang

et al. 2021

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

show abstract

“…It is also an important part in the field of Chinese Natural Language Processing [74][75][76]. It has been used in social multimedia [77][78][79], bio-medicine [80][81][82], medical treatment [83][84][85] and other fields. Due to the particularity of Chinese characters, some Chinese NER methods based on deep learning still have some problems.…”

Section: Challenges and Future Directions Of Chinese Nermentioning

confidence: 99%

A review of Chinese named entity recognition

Cheng

Liu

et al. 2021

KSII TIIS

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Named Entity Recognition (NER) is used to identify entity nouns in the corpus such as Location, Person and Organization, etc. NER is also an important basic of research in various natural language fields. The processing of Chinese NER has some unique difficulties, for example, there is no obvious segmentation boundary between each Chinese character in a Chinese sentence. The Chinese NER task is often combined with Chinese word segmentation, and so on. In response to these problems, we summarize the recognition methods of Chinese NER. In this review, we first introduce the sequence labeling system and evaluation metrics of NER. Then, we divide Chinese NER methods into rule-based methods, statistics-based machine learning methods and deep learning-based methods. Subsequently, we analyze in detail the model framework based on deep learning and the typical Chinese NER methods. Finally, we put forward the current challenges and future research directions of Chinese NER technology.

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“…Machine learning-based named entity recognition is ultimately a classification method [28], but there are two ways to perform it. One is to identify all the named entity boundaries in the medical text first and then classify these entities [29].…”

Section: Related Workmentioning

confidence: 99%

Chinese Medical Entity Recognition Model Based on Character and Word Vector Fusion

Hou

Zhang

et al. 2021

Scientific Programming

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The medical information carried in electronic medical records has high clinical research value, and medical named entity recognition is the key to extracting valuable information from large-scale medical texts. At present, most of the studies on Chinese medical named entity recognition are based on character vector model or word vector model. Owing to the complexity and specificity of Chinese text, the existing methods may fail to achieve good performance. In this study, we propose a Chinese medical named entity recognition method that fuses character and word vectors. The method expresses Chinese texts as character vectors and word vectors separately and fuses them in the model for features. The proposed model can effectively avoid the problems of missing character vector information and inaccurate word vector partitioning. On the CCKS 2019 dataset for the named entity recognition task of Chinese electronic medical records, the proposed model achieves good performance and can effectively improve the accuracy of Chinese medical named entity recognition compared with other baseline models.

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Towards Chinese clinical named entity recognition by dynamic embedding using domain-specific knowledge

Cited by 34 publications

References 21 publications

Extracting Knowledge Entities from Sci-Tech Intelligence Resources Based on BiLSTM and Conditional Random Field

Extracting Knowledge Entities from Sci-Tech Intelligence Resources Based on BiLSTM and Conditional Random Field

A review of Chinese named entity recognition

Chinese Medical Entity Recognition Model Based on Character and Word Vector Fusion

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