In this paper, we introduce StarNet WordNet Editor, an open-source annotation tool designed for natural language processing.It's mainly used for creating and maintaining machinereadable dictionaries like WordNet (Miller, 1995) or domain-specific dictionaries.Word-Net editor provides a user friendly interface and since it is open-source, it is easy to use and develop.Besides English and Turkish WordNet (KeNet) (Bakay et al., 2020), it is also applicable to several languages and their domain specific dictionaries.

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.

Özge Bakay, Özlem Ergelen, Elif Sarmış, Selin Yıldırım, Bilge Nas Arıcan, Atilla Kocabalcıoğlu, Merve Özçelik, Ezgi Sanıyar, Oğuzhan Kuyrukçu, Begüm Avar, Olcay Taner Yıldız. Proceedings of the 11th Global Wordnet Conference. 2021.

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;