2019

  1. Learning from Implicit Information in Natural Language Instructions for Robotic Manipulations Can, Ozan Arkan*, Martires, Pedro Zuidberg*, Persson, Andreas, Gaal, Julian, Loutfi, Amy, De Raedt, Luc, Yuret, Deniz, and Saffiotti, Alessandro In Proceedings of the Combined Workshop on Spatial Language Understanding (SpLU) and Grounded Communication for Robotics (RoboNLP) 2019 [Website] [PDF] [Demo]
  2. Team Howard Beale at SemEval-2019 Task 4: Hyperpartisan News Detection with BERT Mutlu, Osman*, Can, Ozan Arkan*, and Dayanık, Erenay In Proceedings of the 13th International Workshop on Semantic Evaluation 2019 [PDF] [Code]
  3. Learning to Follow Verbal Instructions with Visual Grounding Ünal, Emre, Can, Ozan Arkan, and Yemez, Yücel In 2019 27th Signal Processing and Communications Applications Conference (SIU) 2019 [PDF]
  4. Visually Grounded Language Learning for Robot Navigation Ünal, Emre, Can, Ozan Arkan, and Yemez, Yücel In 1st International Workshop on Multimodal Understanding and Learning for Embodied Applications 2019 [PDF]

2018

  1. A new dataset and model for learning to understand navigational instructions Can, Ozan Arkan, and Yuret, Deniz arXiv preprint arXiv:1805.07952 2018 [PDF] [Slides]

2017

  1. Relational Symbol Grounding through Affordance Learning: An Overview of the ReGround Project Antanas, Laura, Can, Ozan Arkan, Davis, Jesse, De Raedt, Luc, Loutfi, Amy, Persson, Andreas, Saffiotti, Alessandro, Unal, Emre, Yuret, Deniz, and Martires, Pedro Zuidberg In GLU 2017 International Workshop on Grounding Language Understanding 2017 [Website] [PDF] [Slides]

2016

  1. CharNER: Character-level named entity recognition Kuru, Onur, Can, Ozan Arkan, and Yuret, Deniz In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers 2016 [PDF] [Slides] [Code]

2011

  1. Multiword expressions in statistical dependency parsing Eryiğit, Gülşen, Ilbay, Tugay, and Can, Ozan Arkan In Proceedings of the Second Workshop on Statistical Parsing of Morphologically Rich Languages 2011 [PDF]