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.

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About

• Item ORO ID
• 49639
• Item Type
• Conference or Workshop Item
• Project Funding Details

• Funded Project Name	Project ID	Funding Body
  COMRADES	Not Set	EC (European Commission): FP(inc.Horizon2020, H2020, ERC)

• Keywords
• Event Detection, Semantic Deep Learning, Word Embeddings, Semantic Embeddings, CNN, Dual-CNN
• Academic Unit or School
• Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
  Faculty of Science, Technology, Engineering and Mathematics (STEM)
• Research Group
• Centre for Research in Computing (CRC)
• Related URLs
• • http://semdeep.iiia.csic.es/(Other)
• Depositing User
• Harith Alani