Named Entity Recognition in a Very Homogenous Domain

Oshin Agarwal, Ani Nenkova


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.
Anthology ID:
2023.findings-eacl.138
Volume:
Findings of the Association for Computational Linguistics: EACL 2023
Month:
May
Year:
2023
Address:
Dubrovnik, Croatia
Editors:
Andreas Vlachos, Isabelle Augenstein
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1850–1855
Language:
URL:
https://aclanthology.org/2023.findings-eacl.138
DOI:
10.18653/v1/2023.findings-eacl.138
Bibkey:
Cite (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.
Cite (Informal):
Named Entity Recognition in a Very Homogenous Domain (Agarwal & Nenkova, Findings 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.findings-eacl.138.pdf
Video:
 https://aclanthology.org/2023.findings-eacl.138.mp4