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Updated: May 2, 2026
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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
Published on: February 23, 2019
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Annotated corpus for traditional formula-disease relationships in biomedical articles
Sangjun Yea1, Ho Jang2,3, Soyoung Kim2,3
1Korean medicine data division, Korea Institute of Oriental Medicine, Daejeon, 34054, Republic of Korea. tomita@kiom.re.kr.
Scientific Data
|January 8, 2025
Summary
This study introduces the Traditional Formula-Disease Relationship (TFDR) corpus, a new dataset for NLP. It aids in extracting knowledge on traditional formulas and diseases from biomedical literature.
Area of Science:
- Biomedical Informatics
- Traditional Medicine
- Natural Language Processing
Background:
- Traditional Formulas (TF) are gaining global recognition as alternative medicine.
- Extracting knowledge on TF-disease relationships from literature is challenging manually.
- Natural Language Processing (NLP) offers potential for automated knowledge extraction.
Purpose of the Study:
- To introduce the Traditional Formula-Disease Relationship (TFDR) corpus.
- To facilitate automatic extraction of TF-disease relationships using NLP.
- To address the lack of high-quality annotated data in traditional medicine research.
Main Methods:
- Developed a manually annotated corpus (TFDR) from 740 PubMed abstracts.
- Included mentions of Traditional Formulas (TF) and diseases.
- Annotated 1,109 TF-disease relationships within 744 key-sentences.
Main Results:
- The TFDR corpus contains 6,211 TF mentions and 7,166 disease mentions.
- Successfully captured 1,109 relationships between TFs and diseases.
- Provides a valuable resource for NLP applications in traditional medicine.
Conclusions:
- The TFDR corpus is a crucial resource for advancing NLP in traditional medicine.
- Enables automated knowledge discovery of TF-disease interactions.
- Supports integration of traditional medicine insights into biomedical science.
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