Thesis (Selection of subject)Thesis (Selection of subject)(version: 294)
Assignment details
  
Rysy z eye-trackeru v syntaktickém parsingu
Thesis title in Czech: Rysy z eye-trackeru v syntaktickém parsingu
Thesis title in English: Eye-tracking features in syntactic parsing
Key words: eye tracker syntaktický parsing zpracování přirozeného jazyka strojové učení
English key words: eye tracker syntactic parsing natural language processing machine learning
Academic year of topic announcement: 2019/2020
Type of assignment: diploma thesis
Thesis language:
Department: Institute of Formal and Applied Linguistics (32-UFAL)
Supervisor: Mgr. Rudolf Rosa, Ph.D.
Author: hidden - assigned by the advisor
Date of registration: 07.11.2019
Date of assignment: 10.01.2020
Guidelines
The goal of the thesis is to investigate the possibilities of connecting eye-tracking features with syntactic parsing.

The thesis will try to answer one or more of the following questions:
- Can eye tracking features be useful for syntactic parsing?
- Can syntactic features improve prediction of eye tracking features?
- What knowledge do eye tracking features bring into prediction of syntactic categories?
- Could eye tracking features be useful as a cheaper and/or language-independent alternative or supplement to classical treebank annotation?

The thesis will use the Dundee corpus, which contains more than 2000 sentences labeled with eye tracking data as well as morphosyntactic annotation.
References
Strzyz, Michalina, David Vilares, and Carlos Gómez-Rodríguez. "Towards Making a Dependency Parser See." Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Alessandro Lopopolo, Stefan L. Frank, Antal van den Bosch, and Roel Willems. 2019. Dependency parsing with your eyes: Dependency structure predicts eye regressions during reading. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, pages 77–85, Minneapolis, Minnesota. Association for Computational Linguistics.

Nora Hollenstein and Ce Zhang. 2019. Entity recognition at first sight: Improving NER with eye movement information. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1–10, Minneapolis, Minnesota. Association for Computational Linguistics.

Maria Barrett, Joachim Bingel, Nora Hollenstein, Marek Rei, and Anders Søgaard. 2018. Sequence classification with human attention. In Proceedings of the 22nd Conference on Computational Natural Language Learning, pages 302–312, Brussels, Belgium. Association for Computational Linguistics.

Maria Barrett, Joachim Bingel, Frank Keller, and Anders Søgaard. 2016. Weakly supervised part-of speech tagging using eye-tracking data. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 579–584, Berlin, Germany. Association for Computational Linguistics.

Maria Barrett and Anders Søgaard. 2015a. Reading behavior predicts syntactic categories. In Proceedings of the Nineteenth Conference on Computational Natural Language Learning, pages 345–349, Beijing, China. Association for Computational Linguistics.

Alan Kennedy, Robin Hill, and Joel Pynte. 2003. The Dundee corpus. In Proceedings of the 12th European conference on eye movement.
 
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