Papers by Reem Suwaileh
IDRISI-RA: The First Arabic Location Mention Recognition Dataset of Disaster Tweets (2023.acl-long)
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| Challenge: | a low resource language such as Arabic is understudied for geolocation extraction . a recent study found that geolocation is underutilized for low resource languages such as arabic . |
| Approach: | They propose a publicly-available Arabic Location Mention Recognition dataset . it provides human- and automatically-labeled versions of tweets in order of thousands and millions of tweet . |
| Outcome: | The proposed dataset provides human- and automatically-labeled versions in order of thousands and millions of tweets. |
DART: A Large Dataset of Dialectal Arabic Tweets (L18-1)
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| Challenge: | The Arabic language is the fifth most widely spoken language in the world; more than 380 million people speak and write in Arabic. |
| Approach: | They propose to build a large manually-annotated multi-dialect dataset of Arabic tweets that is publicly available. |
| Outcome: | The proposed dataset is well-balanced over five main Arabic dialects: Egyptian, Maghrebi, Levantine, Gulf, and Iraqi. |
Are We Ready for this Disaster? Towards Location Mention Recognition from Crisis Tweets (2020.coling-main)
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| Challenge: | Despite the widespread use of Twitter during emergencies, the majority of tweets do not have geoinformation. |
| Approach: | They propose to use Twitter to train location mention recognition models using different training settings. |
| Outcome: | The results show that training on near or far-away events boosts the performance compared to training on distant events. |