On Semantics and Deep Learning for Event Detection in Crisis Situations

Burel, Gregoire; Saif, Hassan; Fernandez, Miriam and Alani, Harith (2017). On Semantics and Deep Learning for Event Detection in Crisis Situations. In: Workshop on Semantic Deep Learning (SemDeep), at ESWC 2017, 29 May 2017, Portoroz, Slovenia.

URL: http://semdeep.iiia.csic.es/files/SemDeep-17_paper...

Abstract

In this paper, we introduce Dual-CNN, a semantically-enhanced deep learning model to target the problem of event detection in crisis situations from social media data. A layer of semantics is added to a traditional Convolutional Neural Network (CNN) model to capture the contextual information that is generally scarce in short, ill-formed social media messages. Our results show that our methods are able to successfully identify the existence of events, and event types (hurricane, floods, etc.) accurately (> 79% F-measure), but the performance of the model significantly drops (61% F-measure) when identifying fine-grained event-related information (affected individuals, damaged infrastructures, etc.). These results are competitive with more traditional Machine Learning models, such as SVM.

Viewing alternatives

Item Actions

Export

You can export this page using these formats