Mapping Local News Coverage: Precise location extraction in textual news content using fine-tuned BERT based language model

Sarang Gupta, Kumari Nishu


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
Venues:
EMNLP | NLP+CSS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
155–162
Language:
URL:
https://www.aclweb.org/anthology/2020.nlpcss-1.17
DOI:
10.18653/v1/2020.nlpcss-1.17
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.nlpcss-1.17.pdf