@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://preview.aclanthology.org/add-emnlp-2024-awards/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."
}