Filtreler

20202
Filtreleri Sıfırla

Ayarlar

Yazar: Cesur, Neslihan × Konu: Syntactics ×

Arama Sonuçları

Listeleniyor 1 - 2 / 2
  • Küçük Resim
    Yayın
    On building the largest and cross-linguistic Turkish dependency corpus
    (Institute of Electrical and Electronics Engineers Inc., 2020-10-15) Kuzgun, Aslı; Cesur, Neslihan; Arıcan, Bilge Nas; Özçelik, Merve; Marşan, Büşra; Kara, Neslihan; Aslan, Deniz Baran; Yıldız, Olcay Taner
    In this paper, we aim to introduce the dependency annotation process of the largest and the only cross-linguistic Turkish dependency treebank which was translated from the original Penn Treebank corpus. Within the scope of this project, 16.400 sentences have been morphologically and semantically annotated, and the dependency relations were manually carried out by a team of linguists. It is hoped that this project will serve as a base for a successful dependency parser and a system which can automatically perform the bi-directional conversion between constituency and dependency trees.
  • Küçük Resim
    Yayın
    Creating a syntactically felicitous constituency treebank for Turkish
    (Institute of Electrical and Electronics Engineers Inc., 2020-10-15) Kara, Neslihan; Marşan, Büşra; Özçelik, Merve; Arıcan, Bilge Nas; Kuzgun, Aslı; Cesur, Neslihan; Aslan, Deniz Baran; Yıldız, Olcay Taner
    In this study, Bakay et. al [1] and Yildiz et. al.'s [2] work on Turkish constituency treebanks were developed further. Compared to the previous work, the most prominent feature of this study is the fact that every annotation and refinement process is held manually. In addition, constituency treebank created as a result of this study abides by the syntactic rules and typologic features of Turkish while the trees created by previous studies convey only the translated and simply inverted trees that completely ignore the syntactic properties of Turkish. The methodology followed in this study resulted in a significantly more accurate representation of Turkish language and simpler, relatively flatter trees. The straightforward style of trees in this study reduces the complexity and offers a better training dataset for learning algorithms.