Abstract
HTML tags are typically discarded in free text Named Entity Recognition from Web pages. We investigate whether these discarded tags might be used to improve NER performance. We compare Text+Tags sentences with their Text-Only equivalents, over five datasets, two free text segmentation granularities and two NER models. We find an increased F1 performance for Text+Tags of between 0.9% and 13.2% over all datasets, variants and models. This performance increase, over datasets of varying entity types, HTML density and construction quality, indicates our method is flexible and adaptable. These findings imply that a similar technique might be of use in other Web-aware NLP tasks, including the enrichment of deep language models.- Anthology ID:
- 2020.coling-main.36
- Volume:
- Proceedings of the 28th International Conference on Computational Linguistics
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Editors:
- Donia Scott, Nuria Bel, Chengqing Zong
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 407–413
- Language:
- URL:
- https://aclanthology.org/2020.coling-main.36
- DOI:
- 10.18653/v1/2020.coling-main.36
- Cite (ACL):
- Colin Ashby and David Weir. 2020. Leveraging HTML in Free Text Web Named Entity Recognition. In Proceedings of the 28th International Conference on Computational Linguistics, pages 407–413, Barcelona, Spain (Online). International Committee on Computational Linguistics.
- Cite (Informal):
- Leveraging HTML in Free Text Web Named Entity Recognition (Ashby & Weir, COLING 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.coling-main.36.pdf
- Data
- CoNLL 2003