Master Degree / Yüksek Lisans Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/11147/3008
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Master Thesis Combining Persona and Argument in Dialogue(2024) Güzel, Şükrü; Tekir, SelmaThe increasing popularity of personalized dialogue systems has gained momentum as people's desire for human-like interaction grows. This thesis aims to increase persona-consistent responses in personalized dialogue systems. A data augmentation method was used to enhance the persona consistency of dialogue systems. This technique utilized Large Language Models' few-shot learning capabilities to add counterfactual sentences to the dialogue. GPT 3.5 and Llama 2 models were used to generate counterfactual sentences using the few-shot prompting method. The augmentation method was applied to every dialogue in the PersonaChat dataset that did not originally contain a counterfactual sentence. Evaluation using the state-of-the-art personalized dialogue generation study showed that the persona-consistency results of the dataset augmented with the GPT 3.5 model showed better performance when assessed using metrics.Master Thesis Predicting Software Size From Requirements Written in Natural Language: a Generative Ai Approach(01. Izmir Institute of Technology, 2024) Kennouche, Dhıa Eddıne; Demirörs, OnurIn project management, software size measurement represents a critical process aimed at visualizing a project. This quantification is pursued independently of the specific technologies or technical decisions adopted during the project's development phase. Among the various methodologies employed for this purpose, the COSMIC Functional Size Measurement (FSM) and Event Points are used to facilitate such assessments. These methodologies are instrumental in offering a standardized approach for measuring software size, yet they inherently demand a considerable amount of manual effort. Furthermore, these methods require the manual extraction of Objects of Interest and Event Names, adding to the labor-intensive nature of the process. In response to these challenges, this thesis implements a suite of Artificial Intelligence (AI)-based methods that have dramatically transformed the measurement process. These innovative approaches encompass the creation of a Regression Model that predicts software sizes with remarkable accuracy, a Summarization Model that automates the extraction of Event Names, and a finely tuned Large Language Model (LLM) that generates Objects of Interest with a significant precision. The adoption of these AI-driven techniques has proven to be highly successful, substantially minimizing the manual effort traditionally required in software size measurement and thereby greatly enhancing both efficiency and reliability of estimation practices. Together, these AI-based methodologies represent a significant advancement in software size measurements, offering a more streamlined and efficient approach. By reducing the reliance on manual processes, these methods not only enhance the accuracy and reliability of measurements but also contribute to a more agile project management environment.Master Thesis Learning Citation-Aware Representations for Scientific Papers(01. Izmir Institute of Technology, 2024) Çelik, Ege Yiğit; Tekir, SelmaIn the field of Natural Language Processing (NLP), the tasks of understanding and generating scientific documents are highly challenging and have been extensively studied. Comprehending scientific papers can facilitate the generation of their contents. Similarly, understanding the relationships between scientific papers and their citations can be instrumental in generating and predicting citations within the text of scientific works. Moreover, language models equipped with citation-aware representations can be particularly robust for downstream tasks involving scientific literature. This thesis aims to enhance the accuracy of citation predictions within scientific texts. To achieve this, we hide citations within the context of scientific papers using mask tokens and subsequently pre-train the RoBERTa-base language model to predict citations for these masked tokens. We ensure that each citation is treated as a single token to be predicted by the mask-filling language model. Consequently, our models function as language models with citation-aware representations. Furthermore, we propose two alternative techniques for our approach. Our base technique predicts citations using only the contexts from scientific papers, while our global technique incorporates the titles and abstracts of papers alongside the contexts to improve performance. Experimental results demonstrate that our models significantly surpass the state-of-the-art results on two out of four benchmark datasets. However, for the remaining two datasets, our models yield suboptimal results, indicating potential for further improvement. Additionally, we conducted experiments on sampled datasets to examine the effects of inherent factors on the datasets and to identify correlations between these factors and our results.Master Thesis An Event-Based Hidden Makrov Model Approach To News Classification and Sequencing(Izmir Institute of Technology, 2014) Çavuş, Engin; Tekir, SelmaOver the past years the number of published news articles have an excessive increase. In the past, there was less channel of communication. Moreover the articles were classified by the human operators. In the course of time the means of the communication increased and expanded rapidly. The need for an automated news classification tool is inevitable. The text classification is a statistical machine learning procedure that individual text items are placed into groups based on quantitative information. In this study, an event based news classification and sequencing system is proposed, the model is explained. The decision making process is represented. A case study is prepared and analyzed.