4-classification.bib

@inproceedings{olteanu2015expect,
  title={What to expect when the unexpected happens: Social media communications across crises},
  author={Olteanu, Alexandra and Vieweg, Sarah and Castillo, Carlos},
  booktitle={Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work \& Social Computing},
  pages={994--1009},
  year={2015},
  organization={ACM}
}

@article{nguyen2016applications,
  title={Applications of online deep learning for crisis response using social media information},
  author={Nguyen, Dat Tien and Joty, Shafiq and Imran, Muhammad and Sajjad, Hassan and Mitra, Prasenjit},
  journal={arXiv preprint arXiv:1610.01030},
  year={2016}
}

@incollection{auer2007dbpedia,
  title={Dbpedia: A nucleus for a web of open data},
  author={Auer, S{\"o}ren and Bizer, Christian and Kobilarov, Georgi and Lehmann, Jens and Cyganiak, Richard and Ives, Zachary},
  booktitle={The semantic web},
  pages={722--735},
  year={2007},
  publisher={Springer}
}

@inproceedings{mendes2011dbpedia,
  title={DBpedia spotlight: shedding light on the web of documents},
  author={Mendes, Pablo N and Jakob, Max and Garc{\'\i}a-Silva, Andr{\'e}s and Bizer, Christian},
  booktitle={Proceedings of the 7th international conference on semantic systems},
  pages={1--8},
  year={2011},
  organization={ACM}
}

@article{kim2014convolutional,
  title={Convolutional neural networks for sentence classification},
  author={Kim, Yoon},
  journal={arXiv preprint arXiv:1408.5882},
  year={2014}
}

@article{bengio2013representation,
  title={Representation learning: A review and new perspectives},
  author={Bengio, Yoshua and Courville, Aaron and Vincent, Pascal},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  volume={35},
  number={8},
  pages={1798--1828},
  year={2013},
  publisher={IEEE}
}

@inproceedings{le2014distributed,
  title={Distributed representations of sentences and documents},
  author={Le, Quoc and Mikolov, Tomas},
  booktitle={International Conference on Machine Learning},
  pages={1188--1196},
  year={2014}
}

@inproceedings{oro49639,
       booktitle = {Workshop on Semantic Deep Learning (SemDeep), at ESWC 2017},
           month = {May},
           title = {On Semantics and Deep Learning for Event Detection in Crisis Situations},
          author = {Gr{\'e}goire Burel and Hassan Saif and Miriam Fernandez and Harith Alani},
            year = {2017},
        keywords = {Event Detection, Semantic Deep Learning, Word Embeddings, Semantic Embeddings, CNN, Dual-CNN},
             url = {http://oro.open.ac.uk/49639/},
        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 ({\ensuremath{>}} 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.}
}

@inproceedings{oro51726,
       booktitle = {The Semantic Web: ISWC 2017},
           month = {October},
           title = {Semantic Wide and Deep Learning for Detecting Crisis-Information Categories on Social Media},
          author = {Gregoire Burel and Hassan Saif and Harith Alani},
            year = {2017},
             url = {http://oro.open.ac.uk/51726/},
        abstract = {When crises hit, many flog to social media to share or consume information related to the event. Social media posts during crises tend to provide valuable reports on affected people, donation offers, help requests, advice provision, etc. Automatically identifying the category of information (e.g., reports on affected individuals, donations and volunteers) contained in these posts is vital for their efficient handling and consumption by effected communities and concerned organisations. In this paper, we introduce Sem-CNN; a wide and deep Convolutional Neural Network (CNN) model designed for identifying the category of information contained in crisis-related social media content. Unlike previous models, which mainly rely on the lexical representations of words in the text, the proposed model integrates an additional layer of semantics that represents the named entities in the text, into a wide and deep CNN network. Results show that the Sem-CNN model consistently outperforms the baselines which consist of
statistical and non-semantic deep learning models.}
}

@inproceedings{gbureliscram18,
       booktitle = {ISCRAM 2018},
           title = {Crisis Event Extraction Service (CREES) - Automatic Detection and Classification of Crisis-related Content on Social Media},
          author = {Gregoire Burel and and Harith Alani},
            year = {2018}
}