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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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Published on: October 13, 2023

Syntax-based transfer learning for the task of biomedical relation extraction.

Joël Legrand¹, Yannick Toussaint², Chedy Raïssi²

¹Université de Lorraine, CNRS, Inria, LORIA, Nancy, 54000, France. joel.legrand@inria.fr.

Journal of Biomedical Semantics

|August 19, 2021

Summary

This summary is machine-generated.

Transfer learning with TreeLSTM improves relation extraction in biomedical texts, especially when data is scarce. Domain adaptation and syntactic features are key to achieving state-of-the-art performance.

Keywords:

Biomedical relation extraction Deep learning Transfer learning

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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

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Published on: September 20, 2018

Area of Science:

Biomedical Natural Language Processing
Machine Learning
Deep Learning

Background:

Transfer learning reuses data from related problems to enhance machine learning performance.
Domain adaptation specifically leverages data from similar tasks in distinct domains.
Deep learning in Natural Language Processing often requires large annotated corpora, which may be unavailable for specific domains.

Purpose of the Study:

To investigate the effectiveness of transfer learning for relation extraction in biomedical texts using the TreeLSTM model.
To evaluate the impact of domain adaptation on TreeLSTM performance for biomedical relation extraction.
To analyze the role of syntactic features in transfer learning for this task.

Main Methods:

Utilized the TreeLSTM model for relation extraction.
Implemented domain adaptation techniques to transfer knowledge from source to target biomedical domains.
Conducted experiments on multiple biomedical relation extraction tasks with varying data availability.

Main Results:

Achieved superior performance compared to the state of the art on two biomedical relation extraction tasks.
Matched state-of-the-art performance on two additional tasks with limited annotated data.
Demonstrated the significant impact of TreeLSTM, particularly with domain adaptation.

Conclusions:

Transfer learning offers a viable solution for relation extraction in resource-scarce biomedical domains.
Domain adaptation is crucial for maximizing performance when labeled data is limited.
Syntactic features play a vital role in effective transfer learning for biomedical relation extraction.