Neslihan Cesur


2022

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A Learning-Based Dependency to Constituency Conversion Algorithm for the Turkish Language
Büşra Marşan | Oğuz K. Yıldız | Aslı Kuzgun | Neslihan Cesur | Arife B. Yenice | Ezgi Sanıyar | Oğuzhan Kuyrukçu | Bilge N. Arıcan | Olcay Taner Yıldız
Proceedings of the Thirteenth Language Resources and Evaluation Conference

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 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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Introducing StarDust: A UD-based Dependency Annotation Tool
Arife B. Yenice | Neslihan Cesur | Aslı Kuzgun | Olcay Taner Yıldız
Proceedings of the 16th Linguistic Annotation Workshop (LAW-XVI) within LREC2022

This paper aims to introduce StarDust, a new, open-source annotation tool designed for NLP studies. StarDust is designed specifically to be intuitive and simple for the annotators while also supporting the annotation of multiple languages with different morphological typologies, e.g. Turkish and English. This demonstration will mainly focus on our UD-based annotation tool for dependency syntax. Linked to a morphological analyzer, the tool can detect certain annotator mistakes and limit undesired dependency relations as well as offering annotators a quick and effective annotation process thanks to its new simple interface. Our tool can be downloaded from the Github.

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Morpholex Turkish: A Morphological Lexicon for Turkish
Bilge Arican | Aslı Kuzgun | Büşra Marşan | Deniz Baran Aslan | Ezgi Saniyar | Neslihan Cesur | Neslihan Kara | Oguzhan Kuyrukcu | Merve Ozcelik | Arife Betul Yenice | Merve Dogan | Ceren Oksal | Gökhan Ercan | Olcay Taner Yıldız
Proceedings of Globalex Workshop on Linked Lexicography within the 13th Language Resources and Evaluation Conference

MorphoLex is a study in which root, prefix and suffixes of words are analyzed. With MorphoLex, many words can be analyzed according to certain rules and a useful database can be created. Due to the fact that Turkish is an agglutinative language and the richness of its language structure, it offers different analyzes and results from previous studies in MorphoLex. In this study, we revealed the process of creating a database with 48,472 words and the results of the differences in language structure.

2021

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Building the Turkish FrameNet
Büşra Marşan | Neslihan Kara | Merve Özçelik | Bilge Nas Arıcan | Neslihan Cesur | Aslı Kuzgun | Ezgi Sanıyar | Oğuzhan Kuyrukçu | Olcay Taner Yildiz
Proceedings of the 11th Global Wordnet Conference

FrameNet (Lowe, 1997; Baker et al., 1998; Fillmore and Atkins, 1998; Johnson et al., 2001) is a computational lexicography project that aims to offer insight into the semantic relationships between predicate and arguments. Having uses in many NLP applications, FrameNet has proven itself as a valuable resource. The main goal of this study is laying the foundation for building a comprehensive and cohesive Turkish FrameNet that is compatible with other resources like PropBank (Kara et al., 2020) or WordNet (Bakay et al., 2019; Ehsani, 2018; Ehsani et al., 2018; Parlar et al., 2019; Bakay et al., 2020) in the Turkish language.

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From Constituency to UD-Style Dependency: Building the First Conversion Tool of Turkish
Aslı Kuzgun | Oğuz Kerem Yıldız | Neslihan Cesur | Büşra Marşan | Arife Betül Yenice | Ezgi Sanıyar | Oguzhan Kuyrukçu | Bilge Nas Arıcan | Olcay Taner Yıldız
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

This paper deliberates on the process of building the first constituency-to-dependency conversion tool of Turkish. The starting point of this work is a previous study in which 10,000 phrase structure trees were manually transformed into Turkish from the original PennTreebank corpus. Within the scope of this project, these Turkish phrase structure trees were automatically converted into UD-style dependency structures, using both a rule-based algorithm and a machine learning algorithm specific to the requirements of the Turkish language. The results of both algorithms were compared and the machine learning approach proved to be more accurate than the rule-based algorithm. The output was revised by a team of linguists. The refined versions were taken as gold standard annotations for the evaluation of the algorithms. In addition to its contribution to the UD Project with a large dataset of 10,000 Turkish dependency trees, this project also fulfills the important gap of a Turkish conversion tool, enabling the quick compilation of dependency corpora which can be used for the training of better dependency parsers.