@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",
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.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="ozyurt-etal-2021-detecting">
<titleInfo>
<title>Detecting Anatomical and Functional Connectivity Relations in Biomedical Literature via Language Representation Models</title>
</titleInfo>
<name type="personal">
<namePart type="given">Ibrahim</namePart>
<namePart type="given">Burak</namePart>
<namePart type="family">Ozyurt</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Joseph</namePart>
<namePart type="family">Menke</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Anita</namePart>
<namePart type="family">Bandrowski</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Maryann</namePart>
<namePart type="family">Martone</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2021-jun</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Second Workshop on Scholarly Document Processing</title>
</titleInfo>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Online</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<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.</abstract>
<identifier type="citekey">ozyurt-etal-2021-detecting</identifier>
<identifier type="doi">10.18653/v1/2021.sdp-1.4</identifier>
<location>
<url>https://aclanthology.org/2021.sdp-1.4</url>
</location>
<part>
<date>2021-jun</date>
<extent unit="page">
<start>27</start>
<end>35</end>
</extent>
</part>
</mods>
</modsCollection>
%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
%S Proceedings of the Second Workshop on Scholarly Document Processing
%D 2021
%8 jun
%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 $F_1$ = 72.8% on anatomical connectivity and $F_1$ = 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