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

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.