conftrace_
← Back to papers
2020 AAAI AAAI 2020

On Identifying Hashtags in Disaster Twitter Data

Abstract

Abstract Tweet hashtags have the potential to improve the search for information during disaster events. However, there is a large number of disaster-related tweets that do not have any user-provided hashtags. Moreover, only a small number of tweets that contain actionable hashtags are useful for disaster response. To facilitate progress on automatic identification (or extraction) of disaster hashtags for Twitter data, we construct a unique dataset of disaster-related tweets annotated with hashtags useful for filtering actionable information. Using this dataset, we further investigate Long Short-Term Memory-based models within a Multi-Task Learning framework. The best performing model achieves an F1-score as high as $92.22%$. The dataset, code, and other resources are available on Github.1

🌉 Interdisciplinary Bridge - Artificial Intelligence and Deep Learning and Machine Learning and Natural Language Processing
🧭 Keyword Pioneer - hashtag extraction
🐝 Cross-Pollinator - Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Jishnu Ray Chowdhury , Cornelia Caragea , Doina Caragea

Topics

Artificial Intelligence > Learning Paradigms > Transfer Learning Machine Learning > Core Methods > Classification Machine Learning > Learning Types > Weakly Supervised Learning Machine Learning > Application Areas > Domain Adaptation Natural Language Processing > Applications > Text Classification Machine Learning > Learning Types > Multi-Task Learning Deep Learning > Learning Types > Multi-Task Learning

Keywords

multi-task learning text classification named entity recognition long short-term memory twitter datum disaster management hashtag extraction
Download PDF

Related papers

Enhancing Pointer Network for Sentence Ordering with Pairwise Ordering Predictions 2020
CopyMTL: Copy Mechanism for Joint Extraction of Entities and Relations with Multi-Task Learning 2020
Neural Simile Recognition with Cyclic Multitask Learning and Local Attention 2020
Being Optimistic to Be Conservative: Quickly Learning a CVaR Policy 2020
Multi-Point Semantic Representation for Intent Classification 2020