A learning-based dependency to constituency conversion algorithm for the turkish language

Name: A learning-based dependency to constituency conversion algorithm for the turkish language
Author: Marşan, B., Yıldız, O. K., Kuzgun, A., Yenice, A, Cesur, N., Yenice, A. B., Sanıyar, E., Kuyrukçu, O., Arıcan, B. N., Yıldız, Olcay Taner

İsim	A learning-based dependency to constituency conversion algorithm for the turkish language
Yazar	Marşan, B., Yıldız, O. K., Kuzgun, A., Yenice, A, Cesur, N., Yenice, A. B., Sanıyar, E., Kuyrukçu, O., Arıcan, B. N., Yıldız, Olcay Taner
Basım Tarihi:	2022
Basım Yeri	- European Language Resources Association (ELRA)
Konu	Constituency parsing, Constitueny dpendency conversion, Dependency parsing
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	979-109554672-6
Kayıt Numarası	b0f7f990-ab90-4f28-8356-600c01646dfc
Lokasyon	Computer Science
Tarih	2022
Örnek Metin	This study aims to create the very first dependency-to-constituency conversion algorithm optimised for Turkish language. For this purpose, a state-of-the-art morphologic analyser (Yıldız et al., 2019) and a feature-based machine learning model was used. In order to enhance the performance of the conversion algorithm, bootstrap aggregating meta-algorithm was integrated. While creating the conversation algorithm, typological properties of Turkish were carefully considered. A comprehensive and manually annotated UD-style dependency treebank was the input, and constituency trees were the output of the conversion algorithm. A team of linguists manually annotated a set of constituency trees. These manually annotated trees were used as the gold standard to assess the performance of the algorithm. The conversion process yielded more than 8000 constituency trees whose UD-style dependency trees are also available on GitHub. In addition to its contribution to Turkish treebank resources, this study also offers a viable and easy-to-implement conversion algorithm that can be used to generate new constituency treebanks and training data for NLP resources like constituency parsers.

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A learning-based dependency to constituency conversion algorithm for the turkish language

Yazar Marşan, B., Yıldız, O. K., Kuzgun, A., Yenice, A, Cesur, N., Yenice, A. B., Sanıyar, E., Kuyrukçu, O., Arıcan, B. N., Yıldız, Olcay Taner

Basım Tarihi 2022

Basım Yeri - European Language Resources Association (ELRA)

Konu Constituency parsing, Constitueny dpendency conversion, Dependency parsing

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 979-109554672-6

Kayıt Numarası b0f7f990-ab90-4f28-8356-600c01646dfc

Lokasyon Computer Science

Tarih 2022

Örnek Metin This study aims to create the very first dependency-to-constituency conversion algorithm optimised for Turkish language. For this purpose, a state-of-the-art morphologic analyser (Yıldız et al., 2019) and a feature-based machine learning model was used. In order to enhance the performance of the conversion algorithm, bootstrap aggregating meta-algorithm was integrated. While creating the conversation algorithm, typological properties of Turkish were carefully considered. A comprehensive and manually annotated UD-style dependency treebank was the input, and constituency trees were the output of the conversion algorithm. A team of linguists manually annotated a set of constituency trees. These manually annotated trees were used as the gold standard to assess the performance of the algorithm. The conversion process yielded more than 8000 constituency trees whose UD-style dependency trees are also available on GitHub. In addition to its contribution to Turkish treebank resources, this study also offers a viable and easy-to-implement conversion algorithm that can be used to generate new constituency treebanks and training data for NLP resources like constituency parsers.