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  • BildiriYalnız künye2022
    Boosting Dependency Parsing Performance by Incorporating Additional Features for Agglutinative Languages

    Altıntaş, Mücahit; Tantuğ, A. Cüneyd

    In recent studies, the use of language models has increased noticeably and has made quite good contributions. However, using the proper representation and taking into account the complementary components are still among the issues to be considered. In this research, the impact of sub-word level sentence piece based word representation on the performance of dependency parsing has been demonstrated for agglutinative languages. Furthermore, we propose to use the sentence representation that holds all meaning of the sentence as an additional feature to improve dependency parsing. Our proposed enhancements are experimented on nine agglutinative languages; Estonian, Finnish, Hungarian, Indonesian, Japanese, Kazakh, Korean, Turkish, and Uyghur. We found that the sentence piece based token encoding has contributed parsing performance for the majority of the experimented languages. Using the entire meaning of the sentence as a complementary feature has enhanced parsing performance for six languages out of nine.

Tam Kayıt
AlanDeğerDil
dc.date.accessioned2026-10-29T19:08:59.313801461Z
dc.date.available2026-10-29T19:08:59.313801461Z
dc.identifier.urihttps://rdlopen.rawdatalibrary.net/r/bayburt/11936
dc.rightsinfo:eu-repo/semantics/openAccess
dc.titleAltıntaş, Mücahit
person.affiliation.nameFaculty of Computer Science, Natural Language Processing and Social Robotic Lab, Istanbul Technical University, Maslak, Istanbul, 34469, Turkey, Faculty of Engineering, Bayburt University, Bayburt, 69002, Turkey
person.familyNameAltıntaş
person.givenNameMücahit
person.identifier.scopus57211821236
person.statusunverified