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Yayın English-Turkish parallel semantic annotation of Penn-Treebank(Oficyna Wydawnicza Politechniki Wroclawskiej, 2020) Arıcan, Bilge Nas; Bakay, Özge; Avar, Begüm; Yıldız, Olcay Taner; Ergelen, ÖzlemThis paper reports our efforts in constructing a sense-labeled English-Turkish parallel corpus using the traditional method of manual tagging. We tagged a pre-built parallel treebank which was translated from the Penn Treebank corpus. This approach allowed us to generate a resource combining syntactic and semantic information. We provide statistics about the corpus itself as well as information regarding its development process.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 TanerIn 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.Yayın Constructing a Turkish-English parallel treebank(Association for Computational Linguistics (ACL), 2014) Yıldız, Olcay Taner; Solak, Ercan; Görgün, Onur; Ehsani, RaziehIn this paper, we report our preliminary efforts in building an English-Turkish parallel treebank corpus for statistical machine translation. In the corpus, we manually generated parallel trees for about 5,000 sentences from Penn Treebank. English sentences in our set have a maximum of 15 tokens, including punctuation. We constrained the translated trees to the reordering of the children and the replacement of the leaf nodes with appropriate glosses. We also report the tools that we built and used in our tree translation task.Yayın An all-words sense annotated Turkish corpus(IEEE, 2018-06-06) Akçakaya, Sinan; Yıldız, Olcay TanerThis paper reports our efforts in constructing of a sense labeled Turkish corpus with respect to Turkish Language Institution's dictionary, using the traditional method of manual tagging. We tagged a pre-built parallel treebank which is translated from the Penn Treebank II corpus. This approach allowed us to generate a full-coverage resource, in which syntactic and semantic information merged. We also provide miscellaneous statistics about the corpus itself as well as its development process.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 TanerIn 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.
