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
- 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)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2023.findings-eacl.138.pdf