Abstract
Mapping local news coverage from textual content is a challenging problem that requires extracting precise location mentions from news articles. While traditional named entity taggers are able to extract geo-political entities and certain non geo-political entities, they cannot recognize precise location mentions such as addresses, streets and intersections that are required to accurately map the news article. We fine-tune a BERT-based language model for achieving high level of granularity in location extraction. We incorporate the model into an end-to-end tool that further geocodes the extracted locations for the broader objective of mapping news coverage.- Anthology ID:
- 2020.nlpcss-1.17
- Volume:
- Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- David Bamman, Dirk Hovy, David Jurgens, Brendan O'Connor, Svitlana Volkova
- Venue:
- NLP+CSS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 155–162
- Language:
- URL:
- https://aclanthology.org/2020.nlpcss-1.17
- DOI:
- 10.18653/v1/2020.nlpcss-1.17
- Cite (ACL):
- Sarang Gupta and Kumari Nishu. 2020. Mapping Local News Coverage: Precise location extraction in textual news content using fine-tuned BERT based language model. In Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pages 155–162, Online. Association for Computational Linguistics.
- Cite (Informal):
- Mapping Local News Coverage: Precise location extraction in textual news content using fine-tuned BERT based language model (Gupta & Nishu, NLP+CSS 2020)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2020.nlpcss-1.17.pdf