publications

2025

1. A Semi-Automated Approach to the Annotation of Argument Structures in Turkish Datasets

   Neslihan Cesur, Sabri Ince, Ali Hakkı Aydın, and 7 more authors

   In Proceedings of the 13th Global Wordnet Conference, 2025

   Abs

   This paper presents a PropBank annotation project for Turkish, focusing on core arguments in matrix clauses. Our dataset comprises of five different corpora with 25,580 sentences labeled for verbal predicates and core arguments. Using a semi-automatic approach, we leveraged a dependency layer to pre-assign some ARG0s and ARG1s, followed by manual corrections. Our work will be used in the development of Abstract Meaning Representations (AMRs), enhancing Turkish NLP resources for semantic parsing and higher-level language tasks.

2024

1. Building Annotated Parallel Corpora Using the ATIS Dataset: Two UD-style treebanks in English and Turkish

   Neslihan Cesur, Aslı Kuzgun, Mehmet Kose, and 1 more author

   In Proceedings of the 17th Workshop on Building and Using Comparable Corpora (BUCC) @ LREC-COLING 2024, May 2024

2022

1. Morpholex Turkish: A Morphological Lexicon for Turkish

   Bilge Arican, Aslı Kuzgun, Büşra Marşan, and 11 more authors

   In Proceedings of Globalex Workshop on Linked Lexicography within the 13th Language Resources and Evaluation Conference, Jun 2022

   Abs

   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.

2. Introducing StarDust: A UD-based Dependency Annotation Tool

   Arife B. Yenice, Neslihan Cesur, Aslı Kuzgun, and 1 more author

   In Proceedings of the 16th Linguistic Annotation Workshop (LAW-XVI) within LREC2022, Jun 2022

   Abs

   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.

3. A Learning-Based Dependency to Constituency Conversion Algorithm for the Turkish Language

   Büşra Marşan, Oğuz K. Yıldız, Aslı Kuzgun, and 6 more authors

   In Proceedings of the Thirteenth Language Resources and Evaluation Conference, Jun 2022

   Abs

   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.

2021

1. Building the Turkish FrameNet

   Büşra Marşan, Neslihan Kara, Merve Özçelik, and 6 more authors

   In Proceedings of the 11th Global Wordnet Conference, Jan 2021

   Abs

   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.

2. From Constituency to UD-Style Dependency: Building the First Conversion Tool of Turkish

   Aslı Kuzgun, Oğuz Kerem Yıldız, Neslihan Cesur, and 6 more authors

   In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), Sep 2021

   Abs

   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.

2020

1. Creating A Syntactically Felicitous Constituency Treebank For Turkish

   Neslihan Kara, Büşra Marşan, Merve Özçelik, and 5 more authors

   In 2020 Innovations in Intelligent Systems and Applications Conference (ASYU), Sep 2020

   DOI
2. On Building the Largest and Cross-Linguistic Turkish Dependency Corpus

   Aslı Kuzgun, Neslihan Cesur, Bilge Nas Arıcan, and 5 more authors

   In 2020 Innovations in Intelligent Systems and Applications Conference (ASYU), Sep 2020

   DOI