@inproceedings{cariello-etal-2021-comparison,
title = "A Comparison between Named Entity Recognition Models in the Biomedical Domain",
author = "Cariello, Maria Carmela and
Lenci, Alessandro and
Mitkov, Ruslan",
editor = "Mitkov, Ruslan and
Sosoni, Vilelmini and
Gigu{\`e}re, Julie Christine and
Murgolo, Elena and
Deysel, Elizabeth",
booktitle = "Proceedings of the Translation and Interpreting Technology Online Conference",
month = jul,
year = "2021",
address = "Held Online",
publisher = "INCOMA Ltd.",
url = "https://aclanthology.org/2021.triton-1.9",
pages = "76--84",
abstract = "The domain-specialised application of Named Entity Recognition (NER) is known as Biomedical NER (BioNER), which aims to identify and classify biomedical concepts that are of interest to researchers, such as genes, proteins, chemical compounds, drugs, mutations, diseases, and so on. The BioNER task is very similar to general NER but recognising Biomedical Named Entities (BNEs) is more challenging than recognising proper names from newspapers due to the characteristics of biomedical nomenclature. In order to address the challenges posed by BioNER, seven machine learning models were implemented comparing a transfer learning approach based on fine-tuned BERT with Bi-LSTM based neural models and a CRF model used as baseline. Precision, Recall and F1-score were used as performance scores evaluating the models on two well-known biomedical corpora: JNLPBA and BIOCREATIVE IV (BC-IV). Strict and partial matching were considered as evaluation criteria. The reported results show that a transfer learning approach based on fine-tuned BERT outperforms all others methods achieving the highest scores for all metrics on both corpora.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="cariello-etal-2021-comparison">
<titleInfo>
<title>A Comparison between Named Entity Recognition Models in the Biomedical Domain</title>
</titleInfo>
<name type="personal">
<namePart type="given">Maria</namePart>
<namePart type="given">Carmela</namePart>
<namePart type="family">Cariello</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alessandro</namePart>
<namePart type="family">Lenci</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ruslan</namePart>
<namePart type="family">Mitkov</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2021-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Translation and Interpreting Technology Online Conference</title>
</titleInfo>
<name type="personal">
<namePart type="given">Ruslan</namePart>
<namePart type="family">Mitkov</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Vilelmini</namePart>
<namePart type="family">Sosoni</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Julie</namePart>
<namePart type="given">Christine</namePart>
<namePart type="family">Giguère</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Elena</namePart>
<namePart type="family">Murgolo</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Elizabeth</namePart>
<namePart type="family">Deysel</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>INCOMA Ltd.</publisher>
<place>
<placeTerm type="text">Held Online</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>The domain-specialised application of Named Entity Recognition (NER) is known as Biomedical NER (BioNER), which aims to identify and classify biomedical concepts that are of interest to researchers, such as genes, proteins, chemical compounds, drugs, mutations, diseases, and so on. The BioNER task is very similar to general NER but recognising Biomedical Named Entities (BNEs) is more challenging than recognising proper names from newspapers due to the characteristics of biomedical nomenclature. In order to address the challenges posed by BioNER, seven machine learning models were implemented comparing a transfer learning approach based on fine-tuned BERT with Bi-LSTM based neural models and a CRF model used as baseline. Precision, Recall and F1-score were used as performance scores evaluating the models on two well-known biomedical corpora: JNLPBA and BIOCREATIVE IV (BC-IV). Strict and partial matching were considered as evaluation criteria. The reported results show that a transfer learning approach based on fine-tuned BERT outperforms all others methods achieving the highest scores for all metrics on both corpora.</abstract>
<identifier type="citekey">cariello-etal-2021-comparison</identifier>
<location>
<url>https://aclanthology.org/2021.triton-1.9</url>
</location>
<part>
<date>2021-07</date>
<extent unit="page">
<start>76</start>
<end>84</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T A Comparison between Named Entity Recognition Models in the Biomedical Domain
%A Cariello, Maria Carmela
%A Lenci, Alessandro
%A Mitkov, Ruslan
%Y Mitkov, Ruslan
%Y Sosoni, Vilelmini
%Y Giguère, Julie Christine
%Y Murgolo, Elena
%Y Deysel, Elizabeth
%S Proceedings of the Translation and Interpreting Technology Online Conference
%D 2021
%8 July
%I INCOMA Ltd.
%C Held Online
%F cariello-etal-2021-comparison
%X The domain-specialised application of Named Entity Recognition (NER) is known as Biomedical NER (BioNER), which aims to identify and classify biomedical concepts that are of interest to researchers, such as genes, proteins, chemical compounds, drugs, mutations, diseases, and so on. The BioNER task is very similar to general NER but recognising Biomedical Named Entities (BNEs) is more challenging than recognising proper names from newspapers due to the characteristics of biomedical nomenclature. In order to address the challenges posed by BioNER, seven machine learning models were implemented comparing a transfer learning approach based on fine-tuned BERT with Bi-LSTM based neural models and a CRF model used as baseline. Precision, Recall and F1-score were used as performance scores evaluating the models on two well-known biomedical corpora: JNLPBA and BIOCREATIVE IV (BC-IV). Strict and partial matching were considered as evaluation criteria. The reported results show that a transfer learning approach based on fine-tuned BERT outperforms all others methods achieving the highest scores for all metrics on both corpora.
%U https://aclanthology.org/2021.triton-1.9
%P 76-84
Markdown (Informal)
[A Comparison between Named Entity Recognition Models in the Biomedical Domain](https://aclanthology.org/2021.triton-1.9) (Cariello et al., TRITON 2021)
ACL