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Rysy z eye-trackeru v syntaktickém parsingu
dc.contributor.advisorRosa, Rudolf
dc.creatorAgrawal, Abhishek
dc.date.accessioned2020-10-01T09:54:39Z
dc.date.available2020-10-01T09:54:39Z
dc.date.issued2020
dc.identifier.urihttp://hdl.handle.net/20.500.11956/120858
dc.description.abstractIn this thesis, we explore the potential benefits of leveraging eye-tracking information for dependency parsing on the English part of the Dundee corpus. To achieve this, we cast dependency parsing as a sequence labelling task and then augment the neural model for sequence labelling with eye-tracking features. We also augment a graph-based parser with eye-tracking features and parse the Dundee Corpus to corroborate our findings from the sequence labelling parser. We then experiment with a variety of parser setups ranging from parsing with all features to a delexicalized parser. Our experiments show that for a parser with all features, although the improvements are positive for the LAS score they are not significant whereas our delexicalized parser significantly outperforms the baseline we established. We also analyze the contribution of various eye-tracking features towards the different parser setups and find that eye-tracking features contain information which is complementary in nature, thus implying that augmenting the parser with various gaze features grouped together provides better performance than any individual gaze feature. 1en_US
dc.languageEnglishcs_CZ
dc.language.isoen_US
dc.publisherUniverzita Karlova, Matematicko-fyzikální fakultacs_CZ
dc.subjecteye tracker syntaktický parsing zpracování přirozeného jazyka strojové učenícs_CZ
dc.subjecteye tracker syntactic parsing natural language processing machine learningen_US
dc.titleEye-tracking features in syntactic parsingen_US
dc.typediplomová prácecs_CZ
dcterms.created2020
dcterms.dateAccepted2020-09-10
dc.description.departmentÚstav formální a aplikované lingvistikycs_CZ
dc.description.departmentInstitute of Formal and Applied Linguisticsen_US
dc.description.facultyFaculty of Mathematics and Physicsen_US
dc.description.facultyMatematicko-fyzikální fakultacs_CZ
dc.identifier.repId219879
dc.title.translatedRysy z eye-trackeru v syntaktickém parsingucs_CZ
dc.contributor.refereeStraková, Jana
thesis.degree.nameMgr.
thesis.degree.levelnavazující magisterskécs_CZ
thesis.degree.disciplineComputational Linguisticsen_US
thesis.degree.disciplineMatematická lingvistikacs_CZ
thesis.degree.programComputer Scienceen_US
thesis.degree.programInformatikacs_CZ
uk.thesis.typediplomová prácecs_CZ
uk.taxonomy.organization-csMatematicko-fyzikální fakulta::Ústav formální a aplikované lingvistikycs_CZ
uk.taxonomy.organization-enFaculty of Mathematics and Physics::Institute of Formal and Applied Linguisticsen_US
uk.faculty-name.csMatematicko-fyzikální fakultacs_CZ
uk.faculty-name.enFaculty of Mathematics and Physicsen_US
uk.faculty-abbr.csMFFcs_CZ
uk.degree-discipline.csMatematická lingvistikacs_CZ
uk.degree-discipline.enComputational Linguisticsen_US
uk.degree-program.csInformatikacs_CZ
uk.degree-program.enComputer Scienceen_US
thesis.grade.csVelmi dobřecs_CZ
thesis.grade.enVery gooden_US
uk.abstract.enIn this thesis, we explore the potential benefits of leveraging eye-tracking information for dependency parsing on the English part of the Dundee corpus. To achieve this, we cast dependency parsing as a sequence labelling task and then augment the neural model for sequence labelling with eye-tracking features. We also augment a graph-based parser with eye-tracking features and parse the Dundee Corpus to corroborate our findings from the sequence labelling parser. We then experiment with a variety of parser setups ranging from parsing with all features to a delexicalized parser. Our experiments show that for a parser with all features, although the improvements are positive for the LAS score they are not significant whereas our delexicalized parser significantly outperforms the baseline we established. We also analyze the contribution of various eye-tracking features towards the different parser setups and find that eye-tracking features contain information which is complementary in nature, thus implying that augmenting the parser with various gaze features grouped together provides better performance than any individual gaze feature. 1en_US
uk.file-availabilityV
uk.grantorUniverzita Karlova, Matematicko-fyzikální fakulta, Ústav formální a aplikované lingvistikycs_CZ
thesis.grade.code2
uk.publication-placePrahacs_CZ


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