@inproceedings{ozyurt-etal-2021-detecting,
title = "Detecting Anatomical and Functional Connectivity Relations in Biomedical Literature via Language Representation Models",
author = "Ozyurt, Ibrahim Burak and
Menke, Joseph and
Bandrowski, Anita and
Martone, Maryann",
editor = "Beltagy, Iz and
Cohan, Arman and
Feigenblat, Guy and
Freitag, Dayne and
Ghosal, Tirthankar and
Hall, Keith and
Herrmannova, Drahomira and
Knoth, Petr and
Lo, Kyle and
Mayr, Philipp and
Patton, Robert M. and
Shmueli-Scheuer, Michal and
de Waard, Anita and
Wang, Kuansan and
Wang, Lucy Lu",
booktitle = "Proceedings of the Second Workshop on Scholarly Document Processing",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.sdp-1.4/",
doi = "10.18653/v1/2021.sdp-1.4",
pages = "27--35",
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 $F_1$ = 72.8{\%} on anatomical connectivity and $F_1$ = 74.6{\%} on functional connectivity relation extraction."
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%0 Conference Proceedings
%T Detecting Anatomical and Functional Connectivity Relations in Biomedical Literature via Language Representation Models
%A Ozyurt, Ibrahim Burak
%A Menke, Joseph
%A Bandrowski, Anita
%A Martone, Maryann
%Y Beltagy, Iz
%Y Cohan, Arman
%Y Feigenblat, Guy
%Y Freitag, Dayne
%Y Ghosal, Tirthankar
%Y Hall, Keith
%Y Herrmannova, Drahomira
%Y Knoth, Petr
%Y Lo, Kyle
%Y Mayr, Philipp
%Y Patton, Robert M.
%Y Shmueli-Scheuer, Michal
%Y de Waard, Anita
%Y Wang, Kuansan
%Y Wang, Lucy Lu
%S Proceedings of the Second Workshop on Scholarly Document Processing
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F ozyurt-etal-2021-detecting
%X 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.
%R 10.18653/v1/2021.sdp-1.4
%U https://aclanthology.org/2021.sdp-1.4/
%U https://doi.org/10.18653/v1/2021.sdp-1.4
%P 27-35
Markdown (Informal)
[Detecting Anatomical and Functional Connectivity Relations in Biomedical Literature via Language Representation Models](https://aclanthology.org/2021.sdp-1.4/) (Ozyurt et al., sdp 2021)
ACL