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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 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 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 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.
