6 citations · 8 across the 7 of their papers we have counts for
7 papers
Coping with low data availability for social media crisis message categorisation
Congcong Wang
During crisis situations, social media allows people to quickly share information, including messages requesting help. This can be valuable to emergency responders, who need to cat…
STA: Self-controlled Text Augmentation for Improving Text Classifications
Congcong Wang, Gonzalo Fiz Pontiveros, Steven Derby +1
Despite recent advancements in Machine Learning, many tasks still involve working in low-data regimes which can make solving natural language problems difficult. Recently, a number…
UCD-CS at TREC 2021 Incident Streams Track
Congcong Wang, David Lillis
In recent years, the task of mining important information from social media posts during crises has become a focus of research for the purposes of assisting emergency response (ES)…
Crisis Domain Adaptation Using Sequence-to-sequence Transformers
Congcong Wang, Paul Nulty, David Lillis
User-generated content (UGC) on social media can act as a key source of information for emergency responders in crisis situations. However, due to the volume concerned, computation…
Transformer-based Multi-task Learning for Disaster Tweet Categorisation
Congcong Wang, Paul Nulty, David Lillis
Social media has enabled people to circulate information in a timely fashion, thus motivating people to post messages seeking help during crisis situations. These messages can cont…
Multi-task transfer learning for finding actionable information from crisis-related messages on social media
Congcong Wang, David Lillis
The Incident streams (IS) track is a research challenge aimed at finding important information from social media during crises for emergency response purposes. More specifically, g…