Search Results

Now showing 1 - 3 of 3

Contrastive Retrieval Methodology for Turkish Metaphor Detection and Identification
(Assoc Computing Machinery, 2025) Inan, Emrah
Metaphorical expressions, as a form of figurative language, are individually limited in their use. However, whenboth literal and non-literal meanings are considered, they are frequently used in web content. Hence, producinga balanced dataset to learn superior representations is a challenging task, and metaphor detection suffers froma limited training dataset. To alleviate this problem, we present a retrieval-based contrastive learning approachwhich first identifies candidate metaphors in the input text and then detects metaphorical expressions as aclaim verification task in the inherently unbalanced setting of this study. Furthermore, we adapt contrastivelearning to make it easier to distinguish between the literal and figurative meanings of the same expression.For the experimental setup, we extract non-literal and literal expressions along with their meanings andsample sentences from a Turkish dictionary. In the metaphor detection subtask, performance evaluation shows that sparse and dense search variations using the Turkish-e5-Large model achieve a Recall@10 (R@10) scoreof 0.614. Moreover, the SimCSE-TR-Contr-Sample-Meaning model achieves the highest Recall@10 (R@10)of 0.9739 on the generated test dataset for the metaphor identification subtask. In the real-world scenario,it achieves a competitive R@10 score of 0.8684, and these results clearly demonstrate that our model cangeneralise to this real-world scenario
Making Hierarchically Aware Decisions on Short Findings for Automatic Summarisation
(Elsevier, 2025) Inan, Emrah
An impression in a typical radiology report emphasises critical information by providing a conclusion and reasoning based on the findings. However, the findings and impression sections of these reports generally contain brief texts, as they highlight crucial observations derived from the clinical radiograph. In this scenario, abstractive summarisation models often experience a degradation in performance when generating short impressions. To address this challenge in the summarisation task, our work proposes a method that combines well-known fine-tuned text classification and abstractive summarisation language models. Since fine-tuning a language model requires an extensive, well-defined training dataset and is a time-consuming task dependent on high GPU resources, we employ prompt engineering, which uses prompt templates to programme language models and improve their performance. Our method first predicts whether the given findings text is normal or abnormal by leveraging a fine-tuned language model. Then, we apply a radiology-specific BART model to generate the summary for abnormal findings. In the zero-shot setting, our method achieves remarkable results compared to existing approaches on a real-world dataset. In particular, our method achieves scores of 37.43 for ROUGE-1, 21.72 for ROUGE-2, and 35.52 for ROUGE-L.
Digital Transformation in Leather Color Fastness Evaluation: Computer-Assisted Grey Scale Analysis
(Korean Fiber Soc, 2025) Efendioglu, Nilay Ork; Mutlu, Mehmet Mete; Inan, Emrah; Ozgunay, Hasan
Leather is a critical material in the fashion industry, where it is required to meet specific customer demands for color, specifications, and performance, especially regarding color fastness. Traditional methods for assessing color fastness rely on subjective evaluations conducted by professional experts using grey scale standards. However, human evaluation can be inconsistent due to various factors, such as lighting conditions and individual perception. In this study, leather samples were first subjected to expert evaluations and scored using the grey scale system. These evaluations were then compared with color measurement data obtained through a spectrophotometer, which was processed using custom-designed software (written in the Python programming language). This software provided precise grey scale values based on the color measurements, enabling accurate digital assessments. The results of the comparative analysis showed that the computer-assisted grey scale assessment could be completed in a significantly shorter time frame with a minimal margin of error, offering a more reliable and efficient alternative to traditional evaluation methods. This approach not only enhances the accuracy of color assessments but also streamlines the evaluation process in the leather industry.

Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Browse

Filters

Settings

Sort By

Results per page

Search Results