Onur Güngör

Also published as: Onur Gungor


2024

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TURNA: A Turkish Encoder-Decoder Language Model for Enhanced Understanding and Generation
Gökçe Uludoğan | Zeynep Balal | Furkan Akkurt | Meliksah Turker | Onur Gungor | Susan Üsküdarlı
Findings of the Association for Computational Linguistics ACL 2024

The recent advances in natural language processing have predominantly favored well-resourced English-centric models, resulting in a significant gap with low-resource languages. In this work, we introduce TURNA, a language model developed for the low-resource language Turkish and is capable of both natural language understanding and generation tasks.TURNA is pretrained with an encoder-decoder architecture based on the unified framework UL2 with a diverse corpus that we specifically curated for this purpose. We evaluated TURNA with three generation tasks and five understanding tasks for Turkish. The results show that TURNA outperforms several multilingual models in both understanding and generation tasks and competes with monolingual Turkish models in understanding tasks.

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Evaluating the Quality of a Corpus Annotation Scheme Using Pretrained Language Models
Furkan Akkurt | Onur Gungor | Büşra Marşan | Tunga Gungor | Balkiz Ozturk Basaran | Arzucan Özgür | Susan Uskudarli
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Pretrained language models and large language models are increasingly used to assist in a great variety of natural language tasks. In this work, we explore their use in evaluating the quality of alternative corpus annotation schemes. For this purpose, we analyze two alternative annotations of the Turkish BOUN treebank, versions 2.8 and 2.11, in the Universal Dependencies framework using large language models. Using a suitable prompt generated using treebank annotations, large language models are used to recover the surface forms of sentences. Based on the idea that the large language models capture the characteristics of the languages, we expect that the better annotation scheme would yield the sentences with higher success. The experiments conducted on a subset of the treebank show that the new annotation scheme (2.11) results in a successful recovery percentage of about 2 points higher. All the code developed for this work is available at https://github.com/boun-tabi/eval-ud .

2023

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TULAP - An Accessible and Sustainable Platform for Turkish Natural Language Processing Resources
Susan Uskudarli | Muhammet Şen | Furkan Akkurt | Merve Gürbüz | Onur Gungor | Arzucan Özgür | Tunga Güngör
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations

Access to natural language processing resources is essential for their continuous improvement. This can be especially challenging in educational institutions where the software development effort required to package and release research outcomes may be overwhelming and under-recognized. Access towell-prepared and reliable research outcomes is important both for their developers as well as the greater research community. This paper presents an approach to address this concern with two main goals: (1) to create an open-source easily deployable platform where resources can be easily shared and explored, and (2) to use this platform to publish open-source Turkish NLP resources (datasets and tools) created by a research lab. The Turkish Natural Language Processing (TULAP) was designed and developed as an easy-to-use platform to share dataset and tool resources which supports interactive tool demos. Numerous open access Turkish NLP resources have been shared on TULAP. All tools are containerized to support portability for custom use. This paper describes the design, implementation, and deployment of TULAP with use cases (available at https://tulap.cmpe.boun.edu.tr/). A short video demonstrating our system is available at https://figshare.com/articles/media/TULAP_Demo/22179047.

2021

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Overcoming the challenges in morphological annotation of Turkish in universal dependencies framework
Talha Bedir | Karahan Şahin | Onur Gungor | Suzan Uskudarli | Arzucan Özgür | Tunga Güngör | Balkiz Ozturk Basaran
Proceedings of the Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop

This paper presents several challenges faced when annotating Turkish treebanks in accordance with the Universal Dependencies (UD) guidelines and proposes solutions to address them. Most of these challenges stem from the lack of adequate support in the UD framework to accurately represent null morphemes and complex derivations, which results in a significant loss of information for Turkish. This loss negatively impacts the tools that are developed based on these treebanks. We raised and discussed these issues within the community on the official UD portal. This paper presents these issues and our proposals to more accurately represent morphosyntactic information for Turkish while adhering to guidelines of UD. This work aims to contribute to the representation of Turkish and other agglutinative languages in UD-based treebanks, which in turn aids to develop more accurately annotated datasets for such languages.

2019

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Detecting Clitics Related Orthographic Errors in Turkish
Ugurcan Arikan | Onur Gungor | Suzan Uskudarli
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

For the spell correction task, vocabulary based methods have been replaced with methods that take morphological and grammar rules into account. However, such tools are fairly immature, and, worse, non-existent for many low resource languages. Checking only if a word is well-formed with respect to the morphological rules of a language may produce false negatives due to the ambiguity resulting from the presence of numerous homophonic words. In this work, we propose an approach to detect and correct the “de/da” clitic errors in Turkish text. Our model is a neural sequence tagger trained with a synthetically constructed dataset consisting of positive and negative samples. The model’s performance with this dataset is presented according to different word embedding configurations. The model achieved an F1 score of 86.67% on a synthetically constructed dataset. We also compared the model’s performance on a manually curated dataset of challenging samples that proved superior to other spelling correctors with 71% accuracy compared to the second-best (Google Docs) with and accuracy of 34%.

2018

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Improving Named Entity Recognition by Jointly Learning to Disambiguate Morphological Tags
Onur Güngör | Suzan Uskudarli | Tunga Güngör
Proceedings of the 27th International Conference on Computational Linguistics

Previous studies have shown that linguistic features of a word such as possession, genitive or other grammatical cases can be employed in word representations of a named entity recognition (NER) tagger to improve the performance for morphologically rich languages. However, these taggers require external morphological disambiguation (MD) tools to function which are hard to obtain or non-existent for many languages. In this work, we propose a model which alleviates the need for such disambiguators by jointly learning NER and MD taggers in languages for which one can provide a list of candidate morphological analyses. We show that this can be done independent of the morphological annotation schemes, which differ among languages. Our experiments employing three different model architectures that join these two tasks show that joint learning improves NER performance. Furthermore, the morphological disambiguator’s performance is shown to be competitive.