Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Conference Object A Semantic Search Engine for Turkish and English Research Resources(Institute of Electrical and Electronics Engineers Inc., 2025) İnan, Emrah; Inan, E.; 03.04. Department of Computer Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyResearch 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.Conference Object Citation - Scopus: 1Applying Weighted Graph Embeddings To Turkish Metaphor Detection(Institute of Electrical and Electronics Engineers Inc., 2024) İnan, Emrah; İnan, Emrah; 03.04. Department of Computer Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyMetaphor is a common literary mechanism that allows abstract concepts to be conceptualised using more concrete terminology. Existing methods rely on either end-to-end models or hand-crafted pre-processing steps. Generating well-defined training datasets for supervised models is a time-consuming operation for this type of problem. There is also a lack of pre-processing steps for resource-poor natural languages. In this study, we propose an approach for detecting Turkish metaphorical concepts. Initially, we collect non-literal concepts including their meaning and reference sentences by employing a Turkish dictionary. Secondly, we generate a graph by discovering super-sense relations between sample texts including target metaphorical expressions in Turkish WordNet. We also compute weights for relations based on the path closeness and word occurrences. Finally, we classify the texts by leveraging a weighted graph embedding model. The evaluation setup indicates that the proposed approach reaches the best F1 and Gmean scores of 0.83 and 0.68 for the generated test sets when we use feature vector representations of the Node2Vec model as the input of the logistic regression for detecting metaphors in Turkish texts. © 2024 IEEE.