Named entity recognition and relation extraction with eye tracking and electroencephalography (EEG) features / Fall 2018 - Master Thesis
It has been shown that various Natural Language Processing (NLP) tasks can benefit from cognitive features. We have recorded a new dataset  of simultaneous eye-tracking and EEG recording while subjects are reading natural sentences. Now we want to explore the potential of this data by applying it to two NLP tasks, namely named entity recognition and relation extraction by developing neural networks to solve these tasks. As a baseline, we can use our state-of-the art relation extraction system .
Supervised by Nora Hollenstein
-  Hollenstein, N., Langer, N., Pedroni, A., Troendle, M., Rotsztejn, J., & Zhang, C. (2018, May 28). Zurich Cognitive Language Processing Corpus: A simultaneous EEG and eye-tracking resource to analyze the human reading process. Retrieved from osf.io/q3zws
-  Rotsztejn, J., Hollenstein, N., & Zhang, C. (2018). ETH-DS3Lab at SemEval-2018 Task 7: Effectively Combining Recurrent and Convolutional Neural Networks for Relation Classification and Extraction. arXiv preprint arXiv:1804.02042.