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Yayın An approach to anaylse Turkish syntax at morphosyntactic level(Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, 2025-01-20) Özenç, Berke; Solak, Ercan; Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, Bilgisayar Mühendisliği Doktora Programı; Işık University, School of Graduate Studies, Ph.D. in Computer EngineeringSyntactic analysis allows us to analyse the sentence structure in various ways. Constituency parsing is one of the various ways of conducting syntactic analysis. This parsing method defines sentence structure as hierarchical relationships between words or phrases and represents them in tree form. Constituency parsing employs constituency grammar which defines how constituents combine and form other constituents. In this grammar, any syntactic structure from the sentence to the words is represented by the constituents. Although this approach is designed to focus on universal aspects of the languages, English has always been in its focus. This situation makes the constituency approach miss the details that the morphology puts in the syntax of morphologically rich languages. In this study, we implement an extension for the constituency parsing which overcomes the challenges in parsing of MRL (Morphologically Rich Language). We propose ideas tailored to Turkish, yet they can be used for any language like Turkish. Our extension enables the constituency parsing to start at the morpheme level. Thus, we involve morphemic structures in the parsing process and express their syntactic effects on the structure. We have our implementations by extending the CYK (Cocke Younger Kasami) algorithm. During parsing, we utilize extra rules to transfer the ambiguity in morphology to the parsing. In addition, we designed a morpheme-focused constituency set for Turkish. This set involves affixes, stems and phrases headed by a stem. We demonstrate our work with a mini treebank and the grammar generated from it.Yayın Large language model based automated translation of natural language to SQL(Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, 2025-01-22) Kanburoğlu, Ali Buğra; Tek, Faik Boray; Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, Bilgisayar Mühendisliği Doktora Programı; Işık University, School of Graduate Studies, Ph.D. in Computer EngineeringThe field of Text-to-SQL, which involves converting natural language into SQL queries, has seen significant advancements, but challenges remain, particularly for low-resource languages like Turkish. This thesis introduces three key contributions to address these challenges. Our first contribution is the development and open-access release of TUR2SQL, the first cross-domain Turkish Text-to-SQL dataset, which consists of 10,809 natural language sentences paired with their corresponding SQL queries. We evaluate the performance of SQLNet, a deep learning model specifically designed for this task, and one of the most successful Large Language Models (LLMs), ChatGPT, on this dataset. The results demonstrate the superior performance of ChatGPT. The second major contribution is the construction and publicly available release of TURSpider, the most extensive Turkish Text-to-SQL dataset. TURSpider is built by translating the widely used cross-domain Spider dataset from English to Turkish. This dataset includes complex queries with varying difficulty levels, facilitating the training and comparison of large language models for Turkish Text-to-SQL tasks. Our comparative analysis shows that fine-tuned Turkish LLMs achieve competitive performance, with some models surpassing OpenAI models in query accuracy. To further enhance performance, we apply the Chainof-Feedback (CoF) methodology, demonstrating its effectiveness across multiple models. Finally, we explore the Mixture-of-Agents (MoA) framework, which combines outputs from multiple models to improve the performance of open-source LLMs for Text-to-SQL tasks. By integrating MoA with the CoF technique, we propose MoAF-SQL, an approach that significantly improves performance, particularly on complex queries. Our experiments show that MoAF-SQL achieves competitive results, highlighting its potential to enhance the Text-to-SQL capabilities of open-source LLMs.Yayın Object recognition with competitive convolutional neural networks(Işık Üniversitesi, 2023-06-12) Erkoç, Tuğba; Eskil, M. Taner; Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, Bilgisayar Mühendisliği Doktora Programı; Işık University, School of Graduate Studies, Ph.D. in Computer EngineeringIn recent years, Artificial Intelligence (AI) has achieved impressive results, often surpassing human capabilities in tasks involving language comprehension and visual recognition. Among these, computer vision has experienced remarkable progress, largely due to the introduction of Convolutional Neural Networks (CNNs). CNNs are inspired by the hierarchical structure of the visual cortex and are designed to detect patterns, objects, and complex relationships within visual data. One key advantage is their ability to learn directly from pixel values without the need for domain expertise, which has contributed to their popularity. These networks are trained using supervised backpropagation, a process that calculates gradients of the network’s parameters (weights and biases) with respect to the loss function. While backpropagation enables impressive performance with CNNs, it also presents certain drawbacks. One such drawback is the requirement for large amounts of labeled data. When the available data samples are limited, the gradients estimated from this limited information may not accurately capture the overall data behavior, leading to suboptimal parameter updates. However, obtaining a sufficient quantity of labeled data poses a challenge. Another drawback is the requirement of careful configuration of hyperparameters, including the number of neurons, learning rate, and network architecture. Finding optimal values for these hyperparameters can be a time-consuming process. Furthermore, as the complexity of the task increases, the network architecture becomes deeper and more complex. To effectively train the shallow layers of the network, one must increase the number of epochs and experiment with solutions to prevent vanishing gradients. Complex problems often require a greater number of epochs to learn the intricate patterns and features present in the data. It’s important to note that while CNNs aim to mimic the structure of the visual cortex, the brain’s learning mechanism does not necessarily involve back-propagation. Although CNNs incorporate the layered architecture of the visual cortex, the reliance on backpropagation introduces an artificial learning procedure that may not align with the brain’s actual learning process. Therefore, it is crucial to explore alternative learning paradigms that do not rely on backpropagation. In this dissertation study, a unique approach to unsupervised training for CNNs is explored, setting it apart from previous research. Unlike other unsupervised methods, the proposed approach eliminates the reliance on backpropagation for training the filters. Instead, we introduce a filter extraction algorithm capable of extracting dataset features by processing images only once, without requiring data labels or backward error updates. This approach operates on individual convolutional layers, gradually constructing them by discovering filters. To evaluate the effectiveness of this backpropagation-free algorithm, we design four distinct CNN architectures and conduct experiments. The results demonstrate the promising performance of training without backpropagation, achieving impressive classification accuracies on different datasets. Notably, these outcomes are attained using a single network setup without any data augmentation. Additionally, our study reveals that the proposed algorithm eliminates the need to predefine the number of filters per convolutional layer, as the algorithm automatically determines this value. Furthermore, we demonstrate that filter initialization from a random distribution is unnecessary when backpropagation is not employed during training.
