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master's thesis
Deep machine learning for syntactic annotation projection
Zagreb: University of Zagreb, Faculty of Science, 2018. urn:nbn:hr:217:780941
Marić, Tina
University of Zagreb
Faculty of Science
Department of Mathematics

Institutional repository: Repository of the Faculty of Science

Cite this document

Marić, T. (2018). Deep machine learning for syntactic annotation projection (Master's thesis). Zagreb: University of Zagreb, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:217:780941

Marić, Tina. "Deep machine learning for syntactic annotation projection." Master's thesis, University of Zagreb, Faculty of Science, 2018. https://urn.nsk.hr/urn:nbn:hr:217:780941

Marić, Tina. "Deep machine learning for syntactic annotation projection." Master's thesis, University of Zagreb, Faculty of Science, 2018. https://urn.nsk.hr/urn:nbn:hr:217:780941

Marić, T. (2018). 'Deep machine learning for syntactic annotation projection', Master's thesis, University of Zagreb, Faculty of Science, accessed 27 November 2024, https://urn.nsk.hr/urn:nbn:hr:217:780941

Marić T. Deep machine learning for syntactic annotation projection [Master's thesis]. Zagreb: University of Zagreb, Faculty of Science; 2018 [cited 2024 November 27] Available at: https://urn.nsk.hr/urn:nbn:hr:217:780941

T. Marić, "Deep machine learning for syntactic annotation projection", Master's thesis, University of Zagreb, Faculty of Science, Zagreb, 2018. Available at: https://urn.nsk.hr/urn:nbn:hr:217:780941

Metadata
TitleDeep machine learning for syntactic annotation projection
AuthorTina Marić
MentorGoran Igaly (mentor)
Committee memberGoran Igaly (predsjednik povjerenstva)
Committee memberMladen Vuković (član povjerenstva)
Committee memberMarko Erceg (član povjerenstva)
Committee memberGoranka Nogo (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Science
(Department of Mathematics)
Zagreb
Defense date and country2018-03-01, Croatia
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Mathematics
Abstract
U ovom radu istražuje se prijenosno učenje kroz više jezika s ciljem omogućavanja sintaktičke analize jezika koji nemaju dovoljno označenih podataka za učenje. Najbolji pristupi rješavanju problema uključuju projekciju oznaka sintaktičkih ovisnosti preko paralelnih tekstova, iz jezika koji imaju mnogo označenih podataka za učenje u jezike koji imaju nedovoljno. U prvom poglavlju opisuju se osnovni pojmovi morfološkog označivanja rečenica i parsanja njihovih ovisnosnih stabala kao i način označivanja sintaktičkih ovisnosti. Prvi pristup rješavanja problema projekcije oznaka sintaktičkih ovisnosti je opisan u drugom poglavlju. Zasnovan je na algoritmu predstavljenom u znanstvenom radu Multilingual Projection for Parsing Truly Low-Resource Languages [10]. Predložene su prilagodbe algoritma koje vode poboljšanju rezultata. U trećem poglavlju predstavljena je ideja o upotrebi neuronskih mreža za projekcije oznaka sintaktičkih ovisnosti te nekoliko ideja kojima rad u budućnosti može biti unaprijeđen.
Abstract (english)
The purpose of this thesis was to explore cross-lingual transfer learning to dependency parsing, with a goal of enabling syntactic analysis for low-resource languages. The best approaches involve annotation projection: the transfer of dependency structures via parallel texts, from resource-rich to low-resource languages. In the first chapter, basic concepts of part of speech tagging and dependency parsing are described as well as the way of annotating texts. The first approach to solving an annotation projection problem is described in the second chapter. It is based on the algorithm presented in the paper Multilingual Projection for Parsing Truly Low-Resource Languages [10]. We propose the way of adjusting the existing algorithm which leads to the improvement of results. In the third chapter, the idea how to use neural networks for annotation projection is presented, and also some of the ideas how the work done in this thesis can be extended in the future.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:217:780941
Study programmeTitle: Computer Science and Mathematics
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra računarstva i matematike (magistar/magistra računarstva i matematike)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2018-09-06 10:29:13