Detecting Anatomical and Functional Connectivity Relations in Biomedical Literature via Language Representation Models

Ibrahim Burak Ozyurt, Joseph Menke, Anita Bandrowski, Maryann Martone


Abstract
Understanding of nerve-organ interactions is crucial to facilitate the development of effective bioelectronic treatments. Towards the end of developing a systematized and computable wiring diagram of the autonomic nervous system (ANS), we introduce a curated ANS connectivity corpus together with several neural language representation model based connectivity relation extraction systems. We also show that active learning guided curation for labeled corpus expansion significantly outperforms randomly selecting connectivity relation candidates minimizing curation effort. Our final relation extraction system achieves F1 = 72.8% on anatomical connectivity and F1 = 74.6% on functional connectivity relation extraction.
Anthology ID:
2021.sdp-1.4
Volume:
Proceedings of the Second Workshop on Scholarly Document Processing
Month:
June
Year:
2021
Address:
Online
Venues:
NAACL | sdp
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
27–35
Language:
URL:
https://aclanthology.org/2021.sdp-1.4
DOI:
10.18653/v1/2021.sdp-1.4
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.sdp-1.4.pdf