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AuthorSuwaileh, Reem
AuthorImran, Muhammad
AuthorElsayed, Tamer
AuthorSajjad, Hassan
Available date2024-03-11T06:03:07Z
Publication Date2020
Publication NameCOLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference
ResourceScopus
URIhttp://hdl.handle.net/10576/52850
URIhttp://dx.doi.org/10.18653/v1/2020.coling-main.550
AbstractThe widespread usage of Twitter during emergencies has provided a new opportunity and timely resource to crisis responders for various disaster management tasks. Geolocation information of pertinent tweets is crucial for gaining situational awareness and delivering aid. However, the majority of tweets do not come with geoinformation. In this work, we focus on the task of location mention recognition from crisis-related tweets. Specifically, we investigate the influence of different types of labeled training data on the performance of a BERT-based classification model. We explore several training settings such as combing in- and out-domain data from news articles and general-purpose and crisis-related tweets. Furthermore, we investigate the effect of geospatial proximity while training on near or far-away events from the target event. Using five different datasets, our extensive experiments provide answers to several critical research questions that are useful for the research community to foster research in this important direction. For example, results show that, for training a location mention recognition model, Twitter-based data is preferred over general-purpose data; and crisis-related data is preferred over general-purpose Twitter data. Furthermore, training on data from geographically-nearby disaster events to the target event boosts the performance compared to training on distant events.
SponsorThis work was made possible by the Graduate Sponsorship Research Award (GSRA) # GSRA5-1-0527-18082 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.
Languageen
PublisherAssociation for Computational Linguistics (ACL)
SubjectClassification (of information)
Disaster prevention
Disasters
Social networking (online)
Classification models
Disaster management
Geo-information
Geo-spatial
Geolocations
Labeled training data
Management tasks
News articles
Performance
Situational awareness
Location
TitleAre We Ready for this Disaster? Towards Location Mention Recognition from Crisis Tweets
TypeConference Paper
Pagination6252-6263
dc.accessType Open Access


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