Papers by Sarthak Khanal
Identification of Fine-Grained Location Mentions in Crisis Tweets (2022.lrec-1)
Copied to clipboard
| 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)
Copied to clipboard
| 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)
Copied to clipboard
| 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. |
GunStance: Stance Detection for Gun Control and Gun Regulation (2024.acl-long)
Copied to clipboard
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. |