Péter Ihász earned his Ph.D. in Computer Science and Engineering in 2019. Specialized in Natural Language Processing, his research topics include sentiment analysis, emotion recognition, dialogue act classification and language modelling through supervised and unsupervised deep learning algorithms. He is currently working on the implementation of non-task oriented dialogue systems.
Full list of publications
P. Ihász: A supplementary feature set for sentiment analysis in Japanese dialogues. In Transactions on Asian and Low-Resource Language Information Processing. P. Ihász: Annotation-efficient approaches towards real-time emotion recognition. P. Ihász: Emotion Recognition through Intentional Context. In International Journal of Affective Engineering. P. Ihász: Latent variable-based multiple instance learning towards label-free polarity detection. In Proceedings of The 2nd International Conference on Information Science and System (ICISS 2019).
P. Ihász: Emotions and intentions mediated with dialogue acts. In Proceedings of 2018 5th International Conference on Business and Industrial Research (ICBIR).
P. Ihász: A computational model for conversational Japanese. In Proceedings of 2015 International Conference on Culture and Computing. P. Ihász: The Use of Formal English Language and ICT Input Sources among Japanese University Students. In Proceedings of 2015 International Conference on Culture and Computing.