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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (11): 145-152    DOI: 10.11925/infotech.2096-3467.2021.0671
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Improving PubMedBERT for CID-Entity-Relation Classification Using Text-CNN
Dong Miao1,4,Su Zhongqi2,Zhou Xiaobei3,Lan Xue4,Cui Zhigang5,Cui Lei4()
1Financial Section, China Medical University, Shenyang 110122, China
2China Medical University Library, Shenyang 110122, China
3Institute of Health Sciences, China Medical University, Shenyang 110122, China
4School of Health Management, China Medical University, Shenyang 110122, China
5Nursing School, China Medical University, Shenyang 110122, China
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Abstract  

[Objective] This paper tries to improve the performance of PubMedBERT for CID entity relation classification. [Methods] We proposed a classification model based on PubMedBERT, which was also fine-tuned by Text-CNN. Then, we input entity pairs and sentence pairs to the model. Third, we used PubMedBERT to encode CID texts and obtained their global features. Finally, we captured important local information from the global features with Text-CNN to decide whether entity pairs have CID relation. [Results] The precision, recall and F1 value of this method on the BioCreative V CDR dataset reached 78.3%, 73.5% and 75.8% respectively, which were at least 3.1%, 1.5% and 3.3% higher than other methods. [Limitations] This model only examines CID texts, and more research is needed to evaluate its performance on clinical data or corpus of other domains. [Conclusions] This method can capture the features of CID texts and improve their entity relation classification.

Key wordsCID Entity Relation Classification      PubMedBERT      Text-CNN      Sentence Pair     
Received: 06 July 2021      Published: 23 December 2021
ZTFLH:  TP391  
Corresponding Authors: Cui Lei,ORCID:0000-0001-9479-8225     E-mail: lcui@cmu.edu.cn

Cite this article:

Dong Miao, Su Zhongqi, Zhou Xiaobei, Lan Xue, Cui Zhigang, Cui Lei. Improving PubMedBERT for CID-Entity-Relation Classification Using Text-CNN. Data Analysis and Knowledge Discovery, 2021, 5(11): 145-152.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.0671     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2021/V5/I11/145

Structure of Model
BC5CDR5 Corpus(PMID:354896)
Dataset Document Chemicals Diseases CID
relation
Mention ID Mention ID
Training 500 5203 1467 4182 1965 1038
Development 500 5347 1507 4244 1865 1012
Test 500 5385 1435 4424 1988 1066
Summary of BC5CDR5 Corpus
ID e1 e2 sentence
3693336_1 triazolo manic triazolam-induced brief episodes of secondary mania in a depressed patient. large doses of triazolam repeatedly induced brief episodes of mania in a depressed elderly woman. features of organic mental disorder (delirium) were not present. manic excitement was coincident with the duration of action of triazolam. the possible contribution of the triazolo group to changes in affective status is discussed
Positive Sample of BC5CDR5
ID e1 e2 sentence
3693336_2 triazolo depressed triazolam-induced brief episodes of secondary mania in a depressed patient. large doses of triazolam repeatedly induced brief episodes of mania in a depressed elderly woman. features of organic mental disorder (delirium) were not present. manic excitement was coincident with the duration of action of triazolam. the possible contribution of the triazolo group to changes in affective status is discussed
Negative Sample of BC5CDR5
方法 Precision Recall F1
Best Approach of BioCreative V CDR[24] 55.6% 58.4% 57.0%
LSTM-based[20] 64.9% 49.3% 56.0%
CNN-based[15] 60.9% 59.5% 60.2%
BERT Original 70.1% 67.7% 65.6%
BERT+Text-CNN 71.2% 68.3% 69.7%
ClinicalBERT 70.5% 69.3% 69.8%
ClinicalBERT+Text-CNN 70.9% 70.0% 70.4%
BioBERT 72.0% 70.3% 71.1%
BioBERT+Text-CNN 73.1% 72.0% 72.5%
PubMedBERT 75.2% 69.1% 72.0%
PubMedBERT+Text-CNN 78.3% 73.5% 75.8%
Performance for Models on BC5CDR5 Corpus
Method Precision Recall F1
PubMed Embedding+Text-CNN 62.7% 56.3% 59.3%
Glove Embedding+Text-CNN 60.4% 54.6% 57.4%
PubMedBERT+Text-CNN 78.3% 73.5% 75.8%
Performance for Pre-trained Model and Word Embedding Models
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