Papers by Doina Caragea
Identification of Fine-Grained Location Mentions in Crisis Tweets (2022.lrec-1)
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| Challenge: | Recent studies have focused on identifying informative tweets by individuals affected by a crisis, without considering their specific types. |
| Approach: | They assemble two tweet crisis datasets and manually annotate them with specific location types to facilitate progress on the fine-grained location identification task. |
| Outcome: | The proposed model performs well in both in-domain and cross-domain settings. |
Multi-task Learning to Enable Location Mention Identification in the Early Hours of a Crisis Event (2021.findings-emnlp)
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| Challenge: | Social media is a platform for people to share their concerns and report information as eyewitnesses of events. |
| Approach: | They propose a multi-task learning approach to leverage available annotated data for several related tasks from the crisis domain to improve performance on a main task with limited annotation. |
| Outcome: | The proposed approach improves performance on a task with limited annotated data. |
Stance Detection in COVID-19 Tweets (2021.acl-long)
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| Challenge: | a global pandemic of COVID-19 has forced major changes in our daily lives . a new stance detection dataset is being used to track the stances of Twitter users . |
| Approach: | They use Twitter stance data to collect stances on topics related to the pandemic . they train models to take advantage of large amounts of unlabeled data . |
| Outcome: | The proposed model improves on existing stance detection datasets and unlabeled data. |
Cross-Lingual Disaster-related Multi-label Tweet Classification with Manifold Mixup (2020.acl-srw)
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| Challenge: | Towards this goal, many studies have focused on disaster-related tweet classification. |
| Approach: | They compile a multilingual dataset for multi-label classification of disaster-related tweets . they show that their model generalizes to unseen disasters in the test set . |
| Outcome: | The proposed model generalizes to unseen disasters and improves with Manifold Mixup. |
Multimodal Semi-supervised Learning for Disaster Tweet Classification (2022.coling-1)
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| Challenge: | During natural disasters, people use social media platforms to post information about casualties and damage . annotating data can be burdensome, subjective and expensive . et al., 2018b; sohn e.t., 2020) proposed semi-supervised multimodal approach to improve performance on multimodal tasks. |
| Approach: | They propose a semi-supervised approach to annotate unlabeled data from Twitter . they extend FixMatch algorithm to a multimodal setting to account for subjective data . |
| Outcome: | The proposed approach improves on multimodal disaster tweet classification tasks. |
A MISMATCHED Benchmark for Scientific Natural Language Inference (2025.findings-acl)
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| Challenge: | Existing datasets for scientific NLI are derived from various computer science domains, whereas non-CS domains are completely ignored. |
| Approach: | They propose a scientific natural language inference benchmark called MisMatched that incorporates sentence pairs having an implicit scientific NLI relation into model training. |
| Outcome: | The proposed benchmark covers three non-CS domains and contains 2,700 human annotated sentence pairs. |
GunStance: Stance Detection for Gun Control and Gun Regulation (2024.acl-long)
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Nikesh Gyawali, Iustin Sirbu, Tiberiu Sosea, Sarthak Khanal, Doina Caragea, Traian Rebedea, Cornelia Caragea
| Challenge: | Social media, especially Twitter, has been a melting pot for such debates. |
| Approach: | They propose to annotate tweets relevant to shooting events into three classes: In-Favor, Against, and Neutral. |
| Outcome: | The proposed approach outperforms supervised, semi-supervised, and LLM-based zero-shot models on the dataset. |