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On Semantics and Deep Learning for Event Detection in Crisis Situations

Burel, Grégoire; 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.

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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.

Item Type: Conference or Workshop Item
Project Funding Details:
Funded Project NameProject IDFunding Body
COMRADESNot SetEC (European Commission): FP(inc.Horizon2020, H2020, ERC)
Keywords: Event Detection, Semantic Deep Learning, Word Embeddings, Semantic Embeddings, CNN, Dual-CNN
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Interdisciplinary Research Centre: Centre for Policing Research and Learning (CPRL)
Centre for Research in Computing (CRC)
Related URLs:
Item ID: 49639
Depositing User: Harith Alani
Date Deposited: 19 Jun 2017 14:34
Last Modified: 02 Oct 2017 12:43
URI: http://oro.open.ac.uk/id/eprint/49639
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