Arife Betül Yenice

Also published as: Arife Betul Yenice


2022

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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.

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WordNet and Wikipedia Connection in Turkish WordNet KeNet
Merve Doğan | Ceren Oksal | Arife Betül Yenice | Fatih Beyhan | Reyyan Yeniterzi | Olcay Taner Yıldız
Proceedings of Globalex Workshop on Linked Lexicography within the 13th Language Resources and Evaluation Conference

This paper aims to present WordNet and Wikipedia connection by linking synsets from Turkish WordNet KeNet with Wikipedia and thus, provide a better machine-readable dictionary to create an NLP model with rich data. For this purpose, manual mapping between two resources is realized and 11,478 synsets are linked to Wikipedia. In addition to this, automatic linking approaches are utilized to analyze possible connection suggestions. Baseline Approach and ElasticSearch Based Approach help identify the potential human annotation errors and analyze the effectiveness of these approaches in linking. Adopting both manual and automatic mapping provides us with an encompassing resource of WordNet and Wikipedia connections.

2021

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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.