Paul, K.; Lee, F.; Ali, W. Biomedical Relation Extraction with Forest-Based Tagging Framework. Preprints2023, 2023111817. https://doi.org/10.20944/preprints202311.1817.v1
APA Style
Paul, K., Lee, F., & Ali, W. (2023). Biomedical Relation Extraction with Forest-Based Tagging Framework. Preprints. https://doi.org/10.20944/preprints202311.1817.v1
Chicago/Turabian Style
Paul, K., Fei Lee and Woods Ali. 2023 "Biomedical Relation Extraction with Forest-Based Tagging Framework" Preprints. https://doi.org/10.20944/preprints202311.1817.v1
Abstract
The groundbreaking Advanced Forest-Based Tagging Framework (AFTF) represents a paradigm shift in the domain of information extraction from medical texts. AFTF introduces a novel dual-binary tree structure that redefines how entity-relation triples are extracted. This innovative approach directly tackles the limitations of traditional linear and graph-based methods, effectively addressing challenges related to overlapping triples and computational efficiency. The AFTF model stands as a beacon of excellence, surpassing established baselines by significant margins in comprehensive evaluations on two pivotal medical datasets. Notably, AFTF achieves remarkable improvements in F1 scores, showcasing its prowess in accurate information extraction from complex medical narratives. Beyond its exceptional performance in the medical domain, AFTF exhibits remarkable versatility. This adaptability is vividly demonstrated through its robust performance across three diverse public datasets, further affirming its position as a versatile and reliable solution for information extraction tasks. This paper provides a comprehensive exposition of the AFTF architecture, shedding light on its innovative design principles and its efficient handling of intricate medical texts. AFTF represents a groundbreaking step forward in the realm of information extraction, promising enhanced accuracy, efficiency, and adaptability for a wide range of applications.
Keywords
Information extraction; Biomedical relation extraction; Dual-binary tree structure
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
