ETRI-Knowledge Sharing Plaform

ENGLISH

성과물

논문 검색
구분 SCI
연도 ~ 키워드

상세정보

학술지 Biomedical Text NER Tagging Tool with Web Interface for Generating BERT-Based Fine-Tuning Dataset
Cited 2 time in scopus Download 58 time Share share facebook twitter linkedin kakaostory
저자
박연지, 이민아, 양근재, 박수준, 손채봉
발행일
202212
출처
Applied Sciences, v.12 no.23, pp.1-13
ISSN
2076-3417
출판사
MDPI
DOI
https://dx.doi.org/10.3390/app122312012
협약과제
22JR1200, 유전자-주석-질병간 연관성 분석을 위한 문헌 데이터 마이닝 기술 개발, 박수준
초록
In this paper, a tagging tool is developed to streamline the process of locating tags for each term and manually selecting the target term. It directly extracts the terms to be tagged from sentences and displays it to the user. It also increases tagging efficiency by allowing users to reflect candidate categories in untagged terms. It is based on annotations automatically generated using machine learning. Subsequently, this architecture is fine-tuned using Bidirectional Encoder Representations from Transformers (BERT) to enable the tagging of terms that cannot be captured using Named-Entity Recognition (NER). The tagged text data extracted using the proposed tagging tool can be used as an additional training dataset. The tagging tool, which receives and saves new NE annotation input online, is added to the NER and RE web interfaces using BERT. Annotation information downloaded by the user includes the category (e.g., diseases, genes/proteins) and the list of words associated to the named entity selected by the user. The results reveal that the RE and NER results are improved using the proposed web service by collecting more NE annotation data and fine-tuning the model using generated datasets. Our application programming interfaces and demonstrations are available to the public at via the website link provided in this paper.
KSP 제안 키워드
Annotation data, Application programming interface, Named Entity Recognition, Web service(WS), annotation information, biomedical text, fine-tuning, machine Learning, tagging tool, text data, web interface
본 저작물은 크리에이티브 커먼즈 저작자 표시 (CC BY) 조건에 따라 이용할 수 있습니다.
저작자 표시 (CC BY)