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Yayın Constructing a WordNet for Turkish using manual and automatic annotation(Assoc Computing Machinery, 2018-05) Ehsani, Razieh; Solak, Ercan; Yıldız, Olcay TanerIn this article, we summarize the methodology and the results of our 2-year-long efforts to construct a comprehensive WordNet for Turkish. In our approach, we mine a dictionary for synonym candidate pairs and manually mark the senses in which the candidates are synonymous. We marked every pair twice by different human annotators. We derive the synsets by finding the connected components of the graph whose edges are synonym senses. We also mined Turkish Wikipedia for hypernym relations among the senses. We analyzed the resulting WordNet to highlight the difficulties brought about by the dictionary construction methods of lexicographers. After splitting the unusually large synsets, we used random walk-based clustering that resulted in a Zipfian distribution of synset sizes. We compared our results to BalkaNet and automatic thesaurus construction methods using variation of information metric. Our Turkish WordNet is available online.Yayın Evaluating the English-Turkish parallel treebank for machine translation(TÜBİTAK, 2022-01-19) Görgün, Onur; Yıldız, Olcay TanerThis study extends our initial efforts in building an English-Turkish parallel treebank corpus for statistical machine translation tasks. We manually generated parallel trees for about 17K sentences selected from the Penn Treebank corpus. English sentences vary in length: 15 to 50 tokens including punctuation. We constrained the translation of trees by (i) reordering of leaf nodes based on suffixation rules in Turkish, and (ii) gloss replacement. We aim to mimic human annotator's behavior in real translation task. In order to fill the morphological and syntactic gap between languages, we do morphological annotation and disambiguation. We also apply our heuristics by creating Nokia English-Turkish Treebank (NTB) to address technical document translation tasks. NTB also includes 8.3K sentences in varying lengths. We validate the corpus both extrinsically and intrinsically, and report our evaluation results regarding perplexity analysis and translation task results. Results prove that our heuristics yield promising results in terms of perplexity and are suitable for translation tasks in terms of BLEU scores.Yayın TURSpider: a Turkish Text-to-SQL dataset and LLM-based study(Institute of Electrical and Electronics Engineers Inc., 2024-11-25) Kanburoğlu, Ali Buğra; Tek, Faik BorayThis paper introduces TURSpider, a novel Turkish Text-to-SQL dataset developed through human translation of the widely used Spider dataset, aimed at addressing the current lack of complex, cross-domain SQL datasets for the Turkish language. TURSpider incorporates a wide range of query difficulties, including nested queries, to create a comprehensive benchmark for Turkish Text-to-SQL tasks. The dataset enables cross-language comparison and significantly enhances the training and evaluation of large language models (LLMs) in generating SQL queries from Turkish natural language inputs. We fine-tuned several Turkish-supported LLMs on TURSpider and evaluated their performance in comparison to state-of-the-art models like GPT-3.5 Turbo and GPT-4. Our results show that fine-tuned Turkish LLMs demonstrate competitive performance, with one model even surpassing GPT-based models on execution accuracy. We also apply the Chain-of-Feedback (CoF) methodology to further improve model performance, demonstrating its effectiveness across multiple LLMs. This work provides a valuable resource for Turkish NLP and addresses specific challenges in developing accurate Text-to-SQL models for low-resource languages.
