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Authors: Zi Chen ; Badal Pokharel ; Bingnan Li and Samsung Lim

Affiliation: School of Civil & Environmental Engineering, University of New South Wales, Kensington, Sydney, Australia

Keyword(s): Named Entity Recognition, Location Extraction, Social Media, Deep Learning Model.

Abstract: Texts are a common form to encode location information which can be used crucially in disaster scenarios. While Named Entity Recognition (NER) has been applied to location extraction from formal texts, its performance on informal and colloquial texts such as social media messages is unsatisfactory. The geo-entities in social media are often neglected or categorized into unknown or ‘other’ entity types such as person or organisation. In this paper we proposed a Bidirectional Long Short-Term Memory (LSTM) Neural Netwok to identify location information especially aiming to recognize rarely known local places in social media messages. The contribution of both syntactic and semantic features to the classification results was explored as well. The proposed method was validated on a Twitter dataset collected from typhoon-affected areas, showing promising performance in detecting location information.

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Paper citation in several formats:
Chen, Z.; Pokharel, B.; Li, B. and Lim, S. (2020). Location Extraction from Twitter Messages using Bidirectional Long Short-Term Memory Model. In Proceedings of the 6th International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM; ISBN 978-989-758-425-1; ISSN 2184-500X, SciTePress, pages 45-50. DOI: 10.5220/0009338800450050

@conference{gistam20,
author={Zi Chen. and Badal Pokharel. and Bingnan Li. and Samsung Lim.},
title={Location Extraction from Twitter Messages using Bidirectional Long Short-Term Memory Model},
booktitle={Proceedings of the 6th International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM},
year={2020},
pages={45-50},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009338800450050},
isbn={978-989-758-425-1},
issn={2184-500X},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM
TI - Location Extraction from Twitter Messages using Bidirectional Long Short-Term Memory Model
SN - 978-989-758-425-1
IS - 2184-500X
AU - Chen, Z.
AU - Pokharel, B.
AU - Li, B.
AU - Lim, S.
PY - 2020
SP - 45
EP - 50
DO - 10.5220/0009338800450050
PB - SciTePress

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