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        <title>Named Entity Recognition in a Very Homogenous Domain</title>
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    <abstract>Machine Learning models have lower accuracy when tested on out-of-domain data. Developing models that perform well on several domains or can be quickly adapted to a new domain is an important research area. Domain, however, is a vague term, that can refer to any aspect of data such as language, genre, source and structure. We consider a very homogeneous source of data, specifically sentences from news articles from the same newspaper in English, and collect a dataset of such “in-domain” sentences annotated with named entities. We find that even in such a homogeneous domain, the performance of named entity recognition models varies significantly across news topics. Selection of diverse data, as we demonstrate, is crucial even in a seemingly homogeneous domain.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-eacl.138</identifier>
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    <part>
        <date>2023-05</date>
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            <start>1850</start>
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%0 Conference Proceedings
%T Named Entity Recognition in a Very Homogenous Domain
%A Agarwal, Oshin
%A Nenkova, Ani
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F agarwal-nenkova-2023-named
%X Machine Learning models have lower accuracy when tested on out-of-domain data. Developing models that perform well on several domains or can be quickly adapted to a new domain is an important research area. Domain, however, is a vague term, that can refer to any aspect of data such as language, genre, source and structure. We consider a very homogeneous source of data, specifically sentences from news articles from the same newspaper in English, and collect a dataset of such “in-domain” sentences annotated with named entities. We find that even in such a homogeneous domain, the performance of named entity recognition models varies significantly across news topics. Selection of diverse data, as we demonstrate, is crucial even in a seemingly homogeneous domain.
%R 10.18653/v1/2023.findings-eacl.138
%U https://aclanthology.org/2023.findings-eacl.138
%U https://doi.org/10.18653/v1/2023.findings-eacl.138
%P 1850-1855

Markdown (Informal)

[Named Entity Recognition in a Very Homogenous Domain](https://aclanthology.org/2023.findings-eacl.138) (Agarwal & Nenkova, Findings 2023)

• Named Entity Recognition in a Very Homogenous Domain (Agarwal & Nenkova, Findings 2023)

ACL

• Oshin Agarwal and Ani Nenkova. 2023. Named Entity Recognition in a Very Homogenous Domain. In Findings of the Association for Computational Linguistics: EACL 2023, pages 1850–1855, Dubrovnik, Croatia. Association for Computational Linguistics.