@inproceedings{gupta-nishu-2020-mapping,
title = "Mapping Local News Coverage: Precise location extraction in textual news content using fine-tuned {BERT} based language model",
author = "Gupta, Sarang and
Nishu, Kumari",
editor = "Bamman, David and
Hovy, Dirk and
Jurgens, David and
O'Connor, Brendan and
Volkova, Svitlana",
booktitle = "Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.nlpcss-1.17",
doi = "10.18653/v1/2020.nlpcss-1.17",
pages = "155--162",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Mapping Local News Coverage: Precise location extraction in textual news content using fine-tuned BERT based language model
%A Gupta, Sarang
%A Nishu, Kumari
%Y Bamman, David
%Y Hovy, Dirk
%Y Jurgens, David
%Y O’Connor, Brendan
%Y Volkova, Svitlana
%S Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F gupta-nishu-2020-mapping
%X 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.
%R 10.18653/v1/2020.nlpcss-1.17
%U https://aclanthology.org/2020.nlpcss-1.17
%U https://doi.org/10.18653/v1/2020.nlpcss-1.17
%P 155-162
Markdown (Informal)
[Mapping Local News Coverage: Precise location extraction in textual news content using fine-tuned BERT based language model](https://aclanthology.org/2020.nlpcss-1.17) (Gupta & Nishu, NLP+CSS 2020)
ACL