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Yayın AnlamVer: Semantic model evaluation dataset for Turkish - word similarity and relatedness(Association for Computational Linguistics (ACL), 2018-08-26) Ercan, Gökhan; Yıldız, Olcay TanerIn this paper, we present AnlamVer, which is a semantic model evaluation dataset for Turkish designed to evaluate word similarity and word relatedness tasks while discriminating those two relations from each other. Our dataset consists of 500 word-pairs annotated by 12 human subjects, and each pair has two distinct scores for similarity and relatedness. Word-pairs are selected to enable the evaluation of distributional semantic models by multiple attributes of words and word-pair relations such as frequency, morphology, concreteness and relation types (e.g., synonymy, antonymy). Our aim is to provide insights to semantic model researchers by evaluating models in multiple attributes. We balance dataset word-pairs by their frequencies to evaluate the robustness of semantic models concerning out-of-vocabulary and rare words problems, which are caused by the rich derivational and inflectional morphology of the Turkish language.Yayın Morpholex Turkish: a morphological Lexicon for Turkish(European Language Resources Association (ELRA), 2022-06-25) Arıcan, Bilge Nas; Kuzgun, Aslı; Marşan, Büşra; Aslan, Deniz Baran; Sanıyar, Ezgi; Cesur, Neslihan; Kara, Neslihan; Kuyrukçu, Oğuzhan; Özçelik, Merve; Yenice, Arife Betül; Doğan, Merve; Oksal, Ceren; Ercan, Gökhan; Yıldız, Olcay TanerMorphoLex 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.Yayın Grammar or crammer? the role of morphology in distinguishing orthographically similar but semantically unrelated words(Institute of Electrical and Electronics Engineers Inc., 2025) Ercan, Gökhan; Yıldız, Olcay TanerWe show that n-gram-based distributional models fail to distinguish unrelated words due to the noise in semantic spaces. This issue remains hidden in conventional benchmarks but becomes more pronounced when orthographic similarity is high. To highlight this problem, we introduce OSimUnr, a dataset of nearly one million English and Turkish word-pairs that are orthographically similar but semantically unrelated (e.g., grammar - crammer). These pairs are generated through a graph-based WordNet approach and morphological resources. We define two evaluation tasks - unrelatedness identification and relatedness classification - to test semantic models. Our experiments reveal that FastText, with default n-gram segmentation, performs poorly (below 5% accuracy) in identifying unrelated words. However, morphological segmentation overcomes this issue, boosting accuracy to 68% (English) and 71% (Turkish) without compromising performance on standard benchmarks (RareWords, MTurk771, MEN, AnlamVer). Furthermore, our results suggest that even state-of-the-art LLMs, including Llama 3.3 and GPT-4o-mini, may exhibit noise in their semantic spaces, particularly in highly synthetic languages such as Turkish. To ensure dataset quality, we leverage WordNet, MorphoLex, and NLTK, covering fully derivational morphology supporting atomic roots (e.g., '-co_here+ance+y' for 'coherency'), with 405 affixes in Turkish and 467 in English.
