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Yayın Paragraph and sentence level semantic textual similarity measurement techniques: An application on solving OSYM exam questions(Işık Üniversitesi, 2019-09-06) Açıkgöz, Onur; Yıldız, Olcay Taner; Işık Üniversitesi, Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği Yüksek Lisans ProgramıAn Application on Solving OSYM Exam Questions Semantic textual similarity is a well-known natural language processing (NLP) task which aims to measure the degree of similarity of two texts in terms of meanings. In this thesis, our goal is to investigate best semantic textual similarity measurement modeling techniques for the Turkish language at paragraph-to-sentence and sentence-to-sentence levels. Our plan is to exploit morphological knowledge of the Turkish language as a prior input, by using morphological disambiguation toolkit of our study group which automatically annotates morphological tags of words (word, syllable, roots, etc.) in morpheme-level while disambiguating possible parse-trees at the sentence-level. As an application, we proposed statistical models challenging to solve two special types of offcial OSYM multiple-choice exam questions, which examine comprehension ability of students on textual meanings at sentence-to-sentence and paragraph-to-sentence levels. We constructed a question dataset for evaluation that covers offcial ÖSYM exams with varying degrees of diffculties such as ÖYS, ÖSS, DGS, TEOG, SBS, etc.Yayın Bilingual software requirements tracing using vector space model(SciTePress, 2014) Yıldız, Olcay Taner; Okutan, Ahmet; Solak, ErcanIn the software engineering world, creating and maintaining relationships between byproducts generated during the software lifecycle is crucial. A typical relation is the one that exists between an item in the requirements document and a block in the subsequent system design, i.e. class in the source code. In many software engineering projects, the requirement documentation is prepared in the language of the developers, whereas developers prefer to use the English language in the software development process. In this paper, we use the vector space model to extract traceability links between the requirements written in one language (Turkish) and the implementations of classes in another language (English). The experiments show that, by using a generic translator such as Google translate, we can obtain promising results, which can also be improved by using comment info in the source code.Yayın Big data storage and automated text summarization in Turkish text(Işık Üniversitesi, 2018-06-19) Aysu, Erdinç; Yıldız, Olcay Taner; Işık Üniversitesi, Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği Yüksek Lisans ProgramıThe subject of this study is storing the large datasets in accordance with Big Data ecosystem and to extract the summary sentences of a text in Turkish, apply the automatic text summarization process which is a subtopic of Natural language processing (NLP). For this purpose, Turkish news articles were collected and the study was carried out through these texts. For the performance test of the work done, 50 different news textiles were given to 20 different persons and 3 sentences which were considered important from each other were asked to be selected and their results were compared with each other. Then, the results from the people were compared with the results from this study. As a result of the test process, the summation performance of the work was measured approximately as thirty-six percentage.Yayın Shallow parsing in Turkish(IEEE, 2017) Topsakal, Ozan; Açıkgöz, Onur; Gürkan, Ali Tunca; Kanburoğlu, Ali Buğra; Ertopçu, Burak; Özenç, Berke; Çam, İlker; Avar, Begüm; Ercan, Gökhan; Yıldız, Olcay TanerIn this study, shallow parsing is applied on Turkish sentences. These sentences are used to train and test the per-formances of various learning algorithms with various features specified for shallow parsing in Turkish.Yayın Word sense disambiguation, named entity recognition, and shallow parsing tasks for Turkish(Işık Üniversitesi, 2019-04-02) Topsakal, Ozan; Yıldız, Olcay Taner; Işık Üniversitesi, Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği Yüksek Lisans ProgramıPeople interactions are based on sentences. The process of understanding sentences is thru converging, parsing the words and making sense of words. The ultimate goal of Natural Language Processing is to understand the meaning of sentences. There are three main areas that are the topics of this thesis, namely, Named Entity Recognition, Shallow Parsing, and Word Sense Disambiguation. The Natural Language Processing algorithms that learn entities, like person, location, time etc. are called Named Entity Recognition algorithms. Parsing sentences is one of the biggest challenges in Natural Language Processing. Since time efficiency and accuracy are inversely proportional with each other, one of the best ideas is to use shallow parsing algorithms to deal with this challenge. Many of words have more than one meaning. Recognizing the correct meaning that is used in a sentence is a difficult problem. In Word Sense Disambiguation literature there are lots of algorithms that can help to solve this problem. This thesis tries to find solutions to these three challenges by applying machine learning trained algorithms. Experiments are done on a dataset, containing 9,557 sentences.Yayın KeNet: a comprehensive Turkish wordNet and its applications in text clustering(Işık Üniversitesi, 2018-06-07) Ehsani, Razieh; Yıldız, Olcay Taner; Işık Üniversitesi, Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği Doktora ProgramıIn this thesis, we summarize the methodology and the results of our e?orts to construct a comprehensive WordNet for Turkish. Most languages have access to comprehensive language resources. Traditional resources like bilingual dictionaries, monolingual dictionaries, thesauri and lexicons are developed by lexicographers. As computer processing of languages gain popularity, a new set of resources become necessary. One such resource is WordNet which was initially constructed for English language in Princeton University. A WordNet contains much of the information contained in a classic dictionary, but it also contains additional relationship information. These relations go beyond synonym relation and give information about relations such as a word being“is-a” or “is-a-part-of” another. These semantic relations are used in many text analysis tasks. A WordNet also categorizes words under common concepts. These concepts are called as synsets. As a result of all these, WordNet is a comprehensive dictionary which is readable by the computers and a useful language resource for text analysis and other research based on human language. In Turkish language, our WordNet is not the ?rst. The previous WordNet is part of BalkaNet project which is a multilingual WordNet including Turkish and Balkan languages. BalkaNet contains only common words between these languages, as such BalkaNet does not contain all Turkish words and su?ers from top-down constructing method disadvantages. BalkaNet project has not been updated or expanded in recent years. In this work we construct a Turkish WordNet from scratch using a bottom-up method. In general there are two methods for constructing WordNets. Bottomup method means that we create the WordNet from scratch while top-down approach uses other WordNets by translating them. We use Turkish Contemporary Dictionary (CDT) which is an online Turkish dictionary provided by Turkish Language Institute. Bottom-up approach has its own di?culties, since constructing a WordNet from scratch requires more resources and a lot of e?ort. In this work, we extract synonyms from CDT and ask experts to match common meanings for pairs of synonyms. We developed an application which makes annotation step easier and more accurate. We also use two groups of annotators to measure inter-annotator agreement. We used some automatic approaches to extract semantic relations from Turkish Wikipedia (Vikipedi) and Vikisözlük. We processed CDT to extract candidate synonyms and used rule based approaches to ?nd synonym sets. There is no thesaurus for Turkish, so as an application we construct a thesaurus automatically and measured accuracy with our manually constructed synsets. We named our WordNet “KeNet”. Finally, in this thesis we developed a novel approach to represent a text document in a vector space. This approach uses WordNet semantic relations. This part of thesis is an application of KeNet. We used our approach to represent text documents and implemented two di?erent clustering algorithms over these vectors. We tested our method over Turkish Wikipedia articles, domains of which are labeled by Wikipedia.Yayın Türkçe anlamsal söylem ve cümle benzerliği analizleri için veri kümesi oluşturma yöntemi(IEEE, 2018-12-06) Ercan, Gökhan; Erkek, Orçun; Açıkgöz, Onur; Özçelik, Rıza; Parlar, Selen; Yıldız, Olcay TanerÇalışmamızın amacı Türkçe için paragraf-cümle düzeyinde anlamsal söylem analizi ve paragraf-cümle ve cümle-cümle düzeyinde metinsel benzerlik ölçümlemesi için bir veri kümesi hazırlamaktır.Yayın Morphological analyser for Turkish(Işık Üniversitesi, 2018-01-25) Özenç, Berke; Solak, Ercan; Işık Üniversitesi, Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği Yüksek Lisans ProgramıNatural Language Processing is one one the fields of work in computer science and specializes in text summarization, machine translation and many various topics. Morphology is one of the Natural Language Processing features which analyses the words with its suxes. A words meaning can change according to the sux that it takes. Turkish is an agglutinative language with rich morphological structure and set of suxes. This features of Turkish result in complex morphology structure. In this study, we present an analyser for Modern Anatolian Turkish which has high coverage on suffixes and morphological rules of Turkish. Two-Level transformation method which is convenient to design morphology of a language, consists our base of approach. We used HFST which is a Finite State Transducer implementation, as our implementation technique. The analyser covers all morphological and phonetic rules that exist in Turkish and contains a lexicon which consist of today's Turkish words. The analyser is publicly available and can be used on http://ddil.isikun.edu.tr/mortur.Yayın Extracting meaningful information student surveys with NLP(Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, 2025-01-29) Pourjalil, Kajal; Ekin, Emine; Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, Bilgisayar Mühendisliği Yüksek Lisans Programı; Işık University, School of Graduate Studies, Master’s Program in Computer EngineeringThis thesis applied NLP techniques to analyze and summarize bilingual student feedback collected via end-of-semester surveys. The dataset, which contained open-ended responses in both English and Turkish, required a model adept at preserving linguistic nuances across languages. The Llama 2-7b-hf model, which had been trained explicitly for text generation, was selected for its capability to produce coherent and contextually relevant summaries. Data preprocessing involved organizing metadata such as department, semester, course name, and section number, segregating comments by word count, and removing personal identifiers to ensure privacy. Shorter comments (fewer than ten words) were grouped and summarized using a pipeline from the Transformers library, while longer comments were fine-tuned with metadataspecific prompts for detailed summarization. To further enhance analysis, sentiment classification was performed using the “cardiffnlp/twitter-robertabase-sentiment” model, categorizing feedback into negative, neutral, and positive sentiments. Evaluation metrics included expert reviews, contextual relevance, and logical consistency with the dataset’s sentiment distribution. Compared to previous models, the Llama 2 model demonstrated superior performance in generating complete, coherent summaries while preserving the overall intent and tone of the comments. Ultimately, this research highlighted the effectiveness of LLMs in processing multilingual educational data and their potential to provide actionable insights for improving course content and student experiences.
