Papers by Çağrı Çöltekin
A Treebank of Asia Minor Greek (2024.lrec-main)
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| Challenge: | Asia Minor Greek (AMG) dialects are endangered because of declining speaker base and scarce linguistic resources. |
| Approach: | They propose to annotate a treebank of Pharasiot Greek, one of the Asia Minor Greek (AMG) dialects. |
| Outcome: | The proposed treebank consists of 350 sentences from six fairy tales in Pharasiot Greek. |
CoNLL-UL: Universal Morphological Lattices for Universal Dependency Parsing (L18-1)
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Amir More, Özlem Çetinoğlu, Çağrı Çöltekin, Nizar Habash, Benoît Sagot, Djamé Seddah, Dima Taji, Reut Tsarfaty
| Challenge: | Using the universal dependencies framework, we address the need for a universal representation of morphological analysis that can capture alternative morphology of surface tokens and is compatible with the segmentation and morphologic annotation guidelines prescribed for UD treebanks. |
| Approach: | They propose a new annotation format for word lattices that represent morphological analyses and a resource that obeys this format for a range of typologically different languages. |
| Outcome: | The proposed model can capture alternative morphological analyses of surface tokens and is compatible with the segmentation and morphology guidelines prescribed for UD treebanks. |
A Corpus of Turkish Offensive Language on Social Media (2020.lrec-1)
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| Challenge: | Identifying abusive, offensive, aggressive or in general inappropriate language has recently attracted interest of researchers from academic as well as commercial institutions. |
| Approach: | They propose to classify Turkish offensive language corpus using state-of-the-art annotation methods . they find 19 % of tweets contain some type of offensive language . |
| Outcome: | The proposed corpus of Turkish offensive language is the first of its kind in the world . the results show that 19 % of the tweets contain some type of offensive language . |
Multimodal Fact-Checking with Vision Language Models: A Probing Classifier based Solution with Embedding Strategies (2025.coling-main)
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| Challenge: | Existing fact-checking systems that use text and image information are susceptible to fake news spread by social media platforms. |
| Approach: | They propose a neural probing classifier based on multimodality and embeddings from text and image encoders to represent multimodal content for fact-checking. |
| Outcome: | The proposed classifier outperforms KNN and SVM baselines in leveraging extracted embeddings, highlighting its effectiveness for multimodal fact-checking. |
CoRoSeOf - An Annotated Corpus of Romanian Sexist and Offensive Tweets (2022.lrec-1)
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| Challenge: | Using CoRoSeOf, we manually annotate social media for sexist and offensive language. |
| Approach: | They introduce a large corpus of Romanian social media manually annotated for sexist and offensive language. |
| Outcome: | The proposed corpus contains 39 245 tweets annotated by multiple annotators with an agreement rate of Fleiss’= 0.45 . |
Cross-Lingual Learning vs. Low-Resource Fine-Tuning: A Case Study with Fact-Checking in Turkish (2024.lrec-main)
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| Challenge: | Currently, most of the research on misinformation is focused on the English language . however, there is a scarcity of datasets for other languages, including Turkish . |
| Approach: | They propose a dataset that spans multiple domains and incorporates evidence from three Turkish fact-checking organizations. |
| Outcome: | The proposed dataset has the potential to advance research in the Turkish language. |
Reproduction and Replication: A Case Study with Automatic Essay Scoring (2020.lrec-1)
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| Challenge: | reproducibility of experiments has gained more attention in the NLP community . recent negative reproduction results indicate that published results are not verifiable . |
| Approach: | They propose to reproduce an earlier study of automatic essay scoring for determining the proficiency of second language learners in a multilingual setting. |
| Outcome: | The proposed reproduction of an AES system for determining the proficiency of second language learners in a multilingual setting is compared with the original. |