Named entity recognition from Chinese adverse drug event reports with lexical feature based BiLSTM-CRF and tri-training

Chen, Yao; Zhou, Changjiang; Li, Tianxin; Wu, Hong; Zhao, Xia; Yang, Kai; Liao, Jun

doi:10.1016/j.jbi.2019.103252

Cited by 63 publications

(33 citation statements)

References 37 publications

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“…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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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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“…Radical features of Chinese characters are used to improve the model performance as well. The main purpose of Chen et al [28] was to automatically identify Adverse Drug Reaction (ADR)-related entities from the narrative descriptions of Chinese ADE Reports (ADERs) so as to serve as supplements when evaluating the structured section of cases, which can further assist in ADR evaluation. In this paper, they employed two highly successful NER models of CRF and BiLSTM-CRF, as well as one generated Lexical Feature-based BiLSTM-CRF (LF-BiLSTM-CRF) model to conduct NER tasks respectively in the Chinese ADEPDs in this paper.…”

Section: Related Workmentioning

confidence: 99%

Named entity recognition for Chinese judgment documents based on BiLSTM and CRF

Huang

Deng

et al. 2020

J Image Video Proc.

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Chinese named entity recognition (CNER) in the judicial domain is an important and fundamental task in the analysis of judgment documents. However, only a few researches have been devoted to this task so far. For Chinese named entity recognition in judgment documents, we propose the use a bidirectional long-short-term memory (BiLSTM) model, which uses character vectors and sentence vectors trained by distributed memory model of paragraph vectors (PV-DM). The output of BiLSTM is used by conditional random field (CRF) to tag the input sequence. We also improved the Viterbi algorithm to increase the efficiency of the model by cutting the path with the lowest score. At last, a novel dataset with manual annotations is constructed. The experimental results on our corpus show that the proposed method is effective not only in reducing the computational time, but also in improving the effectiveness of named entity recognition in the judicial domain.

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“…Yet, we can consider that the papers that did not explicitly indicate the language should also be dedicated to the processing of data in English ; • Chinese became the second language processed in medical NLP papers with 17 mentions. Among the papers published in 2019, we can mention Guan et al [3] working on the generation of synthetic medical record texts, Chen et al [4] aiming at identifying named entities, and Zheng et al [5] interested by the detection of medical text similarity ; • French was the third language (seven mentions) as in the work by Lerner et al [6], followed by three other European languages with less than five mentions: German, Italian [7], and Spanish [8] ; • Other languages identified in the abstracts accounted for one or two papers and included both languages spoken by millions of people (Arabic, Portuguese, Russian) and languages spoken by small communities (Basque, Danish, Japanese, Korean, Lithuanian, Persian, Romanian, Turkish, and Urdu).…”

Section: The Languages Addressedmentioning

confidence: 99%

“…Chinese became the second language processed in medical NLP papers with 17 mentions. Among the papers published in 2019, we can mention Guan et al 3 working on the generation of synthetic medical record texts, Chen et al 4 aiming at identifying named entities, and Zheng et al 5 interested by the detection of medical text similarity ;…”

Section: Principal Findingsmentioning

confidence: 99%

“… 16 trained a Bidirectional Encoder Representations from Transformers (BERT) model on biomedical data in order to produce the BioBERT resource, which is a word representation specifically tuned to process biomedical data now widely used within the Medical Informatics area. Chen et al 4 performed a named entity recognition using several models trained through BiLSTM, while Si et al 17 produced contextual embeddings to improve their concept extraction method. A similar idea to make NLP methods more robust consists in using semantic composition to extract concepts from clinical texts 18 .…”

Section: Principal Findingsmentioning

confidence: 99%

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A Year of Papers Using Biomedical Texts:

Grouin

Grabar

2020

Yearb Med Inform

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Objectives: Analyze papers published in 2019 within the medical natural language processing (NLP) domain in order to select the best works of the field. Methods: We performed an automatic and manual pre-selection of papers to be reviewed and finally selected the best NLP papers of the year. We also propose an analysis of the content of NLP publications in 2019. Results: Three best papers have been selected this year including the generation of synthetic record texts in Chinese, a method to identify contradictions in the literature, and the BioBERT word representation. Conclusions: The year 2019 was very rich and various NLP issues and topics were addressed by research teams. This shows the will and capacity of researchers to move towards robust and reproducible results. Researchers also prove to be creative in addressing original issues with relevant approaches.

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Named entity recognition from Chinese adverse drug event reports with lexical feature based BiLSTM-CRF and tri-training

Cited by 63 publications

References 37 publications

A review of Chinese named entity recognition

A review of Chinese named entity recognition

Named entity recognition for Chinese judgment documents based on BiLSTM and CRF

A Year of Papers Using Biomedical Texts:

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