Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Conference Object Reframing Software Log Summarisation as Multi-Label Classification With Encoder-Decoder Transformer Model(Institute of Electrical and Electronics Engineers Inc., 2025) Türkzeybek, F.Z.; Inan, E.As software systems become more advanced and capable of meeting sophisticated demands, they also become more complex. Consequently, software system logs, which are the most effective tool programmers have for understanding system diagnostics and taking appropriate action, become as complicated as the systems that generate them. To address this issue, software system log summarisation processes the logs generated by complex systems and extracts or summarizes their meaning in a more readable, less complex format. Recent improvements in natural language processing, brought about by transformers that evolved into large language models, offer substantial capabilities that can be implemented for log summarisation tasks. In this study, we explore this capability using a transformer-based model to summarize complex software system logs. The experimental results demonstrate that the fine-tuned T5-Small model improves the average ROUGE-1 and ROUGE-L scores of the BART-Large and Pegasus-Large models by approximately 8.46% and 15.37%, respectively. Thus, the average improvement of the fine-tuned T5-Small over the fine-tuned BART-Large and Pegasus-Large models is approximately 11.92% by means of R1 and RL scores with lesser computational cost. © 2025 IEEE.Conference Object Adapting Language Models to Sentiment Analysis for Automatically Translated and Labelled Turkish News Texts(Institute of Electrical and Electronics Engineers Inc., 2025) Serficeli, S.C.; Udunman, B.; Inan, E.The proliferation of news sources makes it difficult to track current events and social events in real time. In order to interpret social events in this context quickly and effectively, it is important to translate news texts provided in different natural languages into Turkish and to perform sentiment analysis on them. The aim of this study is to translate multilingual news texts into Turkish and perform sentiment analysis on these texts. The generated labels were compared and the data that were given the same label by all models were separated as automatically labelled data. This automatic labelling process ensured that the data for which different models produced consistent results were reliably labelled. When the results were evaluated, F1 score of 0.946 was achieved for sentiment analysis using the automatic labelling mechanism for texts translated into Turkish. © 2025 IEEE.Conference Object A Semantic Search Engine for Turkish and English Research Resources(Institute of Electrical and Electronics Engineers Inc., 2025) Karabacak, O.; Inan, E.Research resources are growing in volume at an exponential rate across disciplines and languages. This exponential increase has created a pressing need for intelligent search systems that can help researchers efficiently access relevant academic material. To overcome this issue, this study introduces a bilingual semantic search engine designed to retrieve academic articles written in both Turkish and English. The primary goal is to improve the accuracy and relevance of academic information retrieval by using modern Natural Language Processing techniques. Instead of relying on traditional keyword-based search methods, the system leverages transformer-based sentence embedding models. To capture semantic meaning more effectively, MiniLM-L6v2, paraphrase-multilingual-MiniLM-L12-v2 and multilingual-e5-base models were chosen for their multilingual capabilities and sentence-level embedding performance. To assess the quality of search results, Mean Average Precision (MAP) and Normalized Discounted Cumulative Gain (nDCG) were used. These metrics were calculated for each model across both language groups. Evaluation results show that the multilingual-e5-base model consistently outperformed the other models in both MAP and nDCG scores, demonstrating superior semantic understanding and multilingual alignment. The system also features a simple and responsive Streamlit-based interface that allows for real-time querying and result display. © 2025 IEEE.