@inproceedings{mustafa-etal-2023-annotating,
title = "Annotating {P}ub{M}ed Abstracts with {M}e{SH} Headings using Graph Neural Network",
author = "Mustafa, Faizan and
Boutalbi, Rafika and
Iurshina, Anastasiia",
editor = "Tafreshi, Shabnam and
Akula, Arjun and
Sedoc, Jo{\~a}o and
Drozd, Aleksandr and
Rogers, Anna and
Rumshisky, Anna",
booktitle = "Proceedings of the Fourth Workshop on Insights from Negative Results in NLP",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.insights-1.9",
doi = "10.18653/v1/2023.insights-1.9",
pages = "75--81",
abstract = "The number of scientific publications in the biomedical domain is continuously increasing with time. An efficient system for indexing these publications is required to make the information accessible according to the user{'}s information needs. Task 10a of the BioASQ challenge aims to classify PubMed articles according to the MeSH ontology so that new publications can be grouped with similar preexisting publications in the field without the assistance of time-consuming and costly annotations by human annotators. In this work, we use Graph Neural Network (GNN) in the link prediction setting to exploit potential graph-structured information present in the dataset which could otherwise be neglected by transformer-based models. Additionally, we provide error analysis and a plausible reason for the substandard performance achieved by GNN.",
}
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%0 Conference Proceedings
%T Annotating PubMed Abstracts with MeSH Headings using Graph Neural Network
%A Mustafa, Faizan
%A Boutalbi, Rafika
%A Iurshina, Anastasiia
%Y Tafreshi, Shabnam
%Y Akula, Arjun
%Y Sedoc, João
%Y Drozd, Aleksandr
%Y Rogers, Anna
%Y Rumshisky, Anna
%S Proceedings of the Fourth Workshop on Insights from Negative Results in NLP
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F mustafa-etal-2023-annotating
%X The number of scientific publications in the biomedical domain is continuously increasing with time. An efficient system for indexing these publications is required to make the information accessible according to the user’s information needs. Task 10a of the BioASQ challenge aims to classify PubMed articles according to the MeSH ontology so that new publications can be grouped with similar preexisting publications in the field without the assistance of time-consuming and costly annotations by human annotators. In this work, we use Graph Neural Network (GNN) in the link prediction setting to exploit potential graph-structured information present in the dataset which could otherwise be neglected by transformer-based models. Additionally, we provide error analysis and a plausible reason for the substandard performance achieved by GNN.
%R 10.18653/v1/2023.insights-1.9
%U https://aclanthology.org/2023.insights-1.9
%U https://doi.org/10.18653/v1/2023.insights-1.9
%P 75-81
Markdown (Informal)
[Annotating PubMed Abstracts with MeSH Headings using Graph Neural Network](https://aclanthology.org/2023.insights-1.9) (Mustafa et al., insights-WS 2023)
ACL