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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 Rule-based chunking of semantic roles for Turkish(Işık Üniversitesi, 2018-01-24) Erkoç, Barış Can; Solak, Ercan; Işık Üniversitesi, Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği Yüksek Lisans ProgramıIn our work, we approached to semantic role labeling from a different angle. Contrary to related works, which focused on determining single role like noun phrase or predicate, we worked on all of the roles. We claim that, morphological analysis of a word and its context can be useful for semantic role labeling task. For that, we first determine the possible semantic chunk boundaries by examining the morphological analysis of words and their contexts. For further improvement in determining the boundaries, we do the first process with the combination of the morphological analysis and the boundary output from the first pass. We use these boundaries to create semantic chunks and labeled them according to their content.Yayın All-words word sense disambiguation in Turkish(Işık Üniversitesi, 2019-09-06) Akçakaya, Sinan; Yıldız, Olcay Taner; Işık Üniversitesi, Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği Yüksek Lisans ProgramıWord sense disambiguation (WSD) is the identi cation of the meaning of words in context in a computational manner. The main subject of this study is to implement and compare the WSD results of various supervised classi ers (Naive Bayes, K Nearest Neighbor, Rocchio and C4.5) in all-words setting. To this end, we have constructed an all-words sense annotated Turkish corpus, using traditional method of manual tagging. During the annotation, a pre-built parallel treebank (aligned from Penn Treebank) has been tagged with the senses of Turkish Language Institutions dictionary. The approach of annotating a treebank allowed us to generate a full-coverage resource, in which syntactic and semantic information merged. In the WSD evaluations, three distinct experiments have been organized to determine the efect of using different feature sets on the disambiguation performance. First experiment has been conducted with a simple feature set that includes the fundamental local features. In the second experiment, the initial feature set has been augmented with several effective morphological features, and in the third one, the feature set has further been extended with the syntactic features. Our test results show that all classi ers have achieved better results in parallel to growing feature set. Additionally, integration of syntactic features has proved to be useful for WSD.Yayın Semantic role labeling for Turkish propbank(Işık Üniversitesi, 2019-09-06) Esgel, Volkan; Yıldız, Olcay Taner; Işık Üniversitesi, Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği Yüksek Lisans ProgramıPeople's communication with each other takes place through sentences that combine words with different purposes. Words can gain different meanings with the presence of other words in the sentences in which they take place. With the rapid development of technology, the studies on understanding of human language by computational power have gained speed. These studies are generally referred to Natural Language Processing and their main purpose is to understand the sentences in human communication. The words in the sentence ful l different purposes. Some words describe an event, while other words indicate details of that event. De ning the semantic roles of words is possible with different algorithms. This study rst started by contributing to the process of determining the semantic roles of the word groups in the sentence by manpower. In addition, the semantic roles in the English sentences were parsed and shared on a web site with the marked roles in the Turkish sentences for comparison purposes. Finally, it is tried to measure how the algorithms aiming to nd the semantic roles of the words in the sentence perform automatically for 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.
