A Multigranularity Text Driven Named Entity Recognition CGAN Model for Traditional Chinese Medicine Literatures

Ma, Yuekun; Liu, Yun; Zhang, Dezheng; Zhang, Jiye; Liu, He; Xie, Ying

doi:10.1155/2022/1495841

Cited by 5 publications

(2 citation statements)

References 25 publications

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“…For example, Zhang et al [4] and Deng et al [5] use BiLSTM to capture the context semantic features. Ma et al [6] designed a multi-granularity text encoder to extract the context semantic features of text from multiple dimensions. The above work focuses on the extraction of the overall contextual information of the text.…”

Section: Introductionmentioning

confidence: 99%

A Named Entity Recognition Method Enhanced with Lexicon Information and Text Local Feature

2023

Teh. vjesn.

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At present, Named Entity Recognition (NER) is one of the fundamental tasks for extracting knowledge from traditional Chinese medicine (TCM) texts. The variability of the length of TCM entities and the characteristics of the language of TCM texts lead to ambiguity of TCM entity boundaries. In addition, better extracting and exploiting local features of text can improve the accuracy of named entity recognition. In this paper, we proposed a TCM NER model with lexicon information and text local feature enhancement of text. In this model, a lexicon is introduced to encode the characters in the text to obtain the context-sensitive global semantic representation of the text. The convolutional neural network (CNN) and gate joined collaborative attention network are used to form a text local feature extraction module to capture the important semantic features of local text. Experiments were conducted on two TCM domain datasets and the F1 values are 91.13% and 90.21% respectively.

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Section: Introductionmentioning

confidence: 99%

A Named Entity Recognition Method Enhanced with Lexicon Information and Text Local Feature

2023

Teh. vjesn.

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“…Consequently, the Chinese named entity recognition task for specific domains is more challenging. To address the above problems, Ma et al [5] started from the study of domain text features and designed a multi-granularity text feature extractor to extract contextual semantic information of text from different granularities. In addition, conditional generative adversarial networks are used to generate pseudo-training samples to address the effect of data size on the model, and excellent results have been achieved on several TCM datasets.…”

Section: Introductionmentioning

confidence: 99%

Chinese Named Entity Recognition Method for Domain-Specific Text

2023

Teh. vjesn.

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The Chinese named entity recognition (NER) is a critical task in natural language processing, aiming at identifying and classifying named entities in text. However, the specificity of domain texts and the lack of large-scale labelled datasets have led to the poor performance of NER methods trained on public domain corpora on domain texts. In this paper, a named entity recognition method incorporating sentence semantic information is proposed, mainly by adaptively incorporating sentence semantic information into character semantic information through an attention mechanism and a gating mechanism to enhance entity feature representation while attenuating the noise generated by irrelevant character information. In addition, to address the lack of large-scale labelled samples, we used data self-augmentation methods to expand the training samples. Furthermore, we introduced a Weighted Strategy considering that the low-quality samples generated by the data self-augmentation process can have a negative impact on the model. Experiments on the TCM prescriptions corpus showed that the F1 values of our method outperformed the comparison methods.

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Incorporating Ontological Knowledge into the Multi-Label Classification of Traditional Chinese Medicine Symptom Entities Research (Preprint)

Hu,

Cheng,

et al. 2023

Preprint

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BACKGROUND In the realm of AI-assisted traditional Chinese medicine (TCM) syndrome differentiation and disease diagnosis, precise symptom recognition and classification pose significant challenges. This is because TCM heavily relies on a nuanced understanding of symptoms to guide treatment decisions. However, current entity recognition models grapple with a range of issues, including limitations stemming from label space, resource-intensive computations, a lack of domain expertise to cover diverse symptom descriptions, and an over-reliance on large annotated datasets. Furthermore, the world of TCM symptom labels is vast and complex, with intricate correlations and the added complexity of label imbalance, all contributing to the complexity of the task. OBJECTIVE The goal of this study is to tackle the challenges associated with multi-class symptom entity recognition within the realm of TCM symptoms. To achieve this, we propose a two-stage entity classification approach and leverage ontology knowledge to enhance entity classification. Our approach aims to reduce the model's dependency on annotated data and address issues such as label imbalance and the expansive label space. METHODS We introduce an innovative multi-label entity classification model designed for accurate TCM symptom recognition. We establish a comprehensive TCM symptom ontology framework to standardize symptom descriptions. We use the BERT+BiLSTM+CRF method to identify multiple symptom entities in TCM medical records. In order to gain a deeper understanding of the relationships between multiple symptom entities within the text and the connections between different category labels, we introduce a multi-level correlation feature fusion module. Finally, we adopt a multi-label classification method based on a hierarchical label tree, effectively mitigating the challenges associated with label imbalance within TCM text. RESULTS Using authentic Qihuang TCM electronic medical records, our model significantly enhances efficiency and accuracy in multi-label symptom classification, achieving a Hamming Loss of 2.932 * 10^-2 and a Micro-F1 score of 0.8452. CONCLUSIONS Our study delivers several notable contributions. Firstly, we construct a comprehensive TCM symptom ontology framework, successfully incorporating TCM domain knowledge into the model to bolster its foundational features. Secondly, we employ a multi-label classification approach for entity recognition, capturing the multiple labels and intricate relationships of symptom entities in TCM texts with heightened accuracy. Simultaneously, we introduce a hierarchical label tree to effectively mitigate the impact of symptom label imbalance on the model. Lastly, we introduce a multi-level correlation feature fusion module that comprehensively captures textual information, thereby improving model performance, enhancing label clustering, and ultimately elevating the overall quality and efficiency of the model. These contributions provide an effective methodology for TCM symptom extraction and are poised to make a significant impact on TCM research and practice.

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A Multigranularity Text Driven Named Entity Recognition CGAN Model for Traditional Chinese Medicine Literatures

Cited by 5 publications

References 25 publications

A Named Entity Recognition Method Enhanced with Lexicon Information and Text Local Feature

A Named Entity Recognition Method Enhanced with Lexicon Information and Text Local Feature

Chinese Named Entity Recognition Method for Domain-Specific Text

Incorporating Ontological Knowledge into the Multi-Label Classification of Traditional Chinese Medicine Symptom Entities Research (Preprint)

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