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Detail práce
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Eye-tracking features in syntactic parsing
Název práce v češtině: Rysy z eye-trackeru v syntaktickém parsingu
Název v anglickém jazyce: Eye-tracking features in syntactic parsing
Klíčová slova: eye tracker syntaktický parsing zpracování přirozeného jazyka strojové učení
Klíčová slova anglicky: eye tracker syntactic parsing natural language processing machine learning
Akademický rok vypsání: 2019/2020
Typ práce: diplomová práce
Jazyk práce: angličtina
Ústav: Ústav formální a aplikované lingvistiky (32-UFAL)
Vedoucí / školitel: Mgr. Rudolf Rosa, Ph.D.
Řešitel: skrytý - zadáno a potvrzeno stud. odd.
Datum přihlášení: 07.11.2019
Datum zadání: 10.01.2020
Datum potvrzení stud. oddělením: 24.03.2020
Datum a čas obhajoby: 10.09.2020 09:00
Datum odevzdání elektronické podoby:30.07.2020
Datum odevzdání tištěné podoby:28.05.2020
Datum proběhlé obhajoby: 10.09.2020
Oponenti: RNDr. Jana Straková, Ph.D.
 
 
 
Zásady pro vypracování
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.
Seznam odborné literatury
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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