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Yayın Sense distinction using computational methods in Turkish dictionaries(Işık Üniversitesi, 2018-01-25) Ertopçu, Burak; Solak, Ercan; Işık Üniversitesi, Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği Yüksek Lisans ProgramıNLP(Natural Language Processing) refers to general name of the study elds related with processing languages by using computer-based systems. In NLP studies, dictionaries are required as lexical and semantic resources. Because in some cases, there are necessities to match the words with their correct senses for all possible words. There are some electronic dictionaries for Turkish such as \Contemporary Turkish Dictionary(CTD)" and \Kubbealt Turkish Dictionary". However, both of these two dictionaries cover similar and redundant senses for several words. There are 86.382 words exist in CTD that written by Turkish Linguistic Society( TDK). There can be more than ten senses for a single word in some cases. By that reason, it can be hard to determine which meanings are explanatory and/or required and which of them are multiplexed needlessly. This problem of finding distinguishing senses of the words is called as \Sense Distinction Problem". The aim of this study is to simplify the sense distinction decisions by using some computational methods. In this study, we focused on to analyse the similarities of word senses by using some computational methods such as; Edit Distance, Cosine Similarity and Jaccard Index Similarity on two well-known Turkish Dictionaries Contemporary Turkish Dictionary (CTD) and Kubbealt Dictionary (KD).Yayın A new approach for named entity recognition(IEEE, 2017) Ertopçu, Burak; Kanburoğlu, Ali Buğra; Topsakal, Ozan; Açıkgöz, Onur; Gürkan, Ali Tunca; Özenç, Berke; Çam, İlker; Avar, Begüm; Ercan, Gökhan; Yıldız, Olcay TanerMany sentences create certain impressions on people. These impressions help the reader to have an insight about the sentence via some entities. In NLP, this process corresponds to Named Entity Recognition (NER). NLP algorithms can trace a lot of entities in the sentence like person, location, date, time or money. One of the major problems in these operations are confusions about whether the word denotes the name of a person, a location or an organisation, or whether an integer stands for a date, time or money. In this study, we design a new model for NER algorithms. We train this model in our predefined dataset and compare the results with other models. In the end we get considerable outcomes in a dataset containing 1400 sentences.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 All-words word sense disambiguation for Turkish(IEEE, 2017) Açıkgöz, Onur; Gürkan, Ali Tunca; Ertopçu, Burak; Topsakal, Ozan; Özenç, Berke; Kanburoğlu, Ali Buğra; Çam, İlker; Avar, Begüm; Ercan, Gökhan; Yıldız, Olcay TanerIdentifying the sense of a word within a context is a challenging problem and has many applications in natural language processing. This assignment problem is called word sense disambiguation(WSD). Many papers in the literature focus on English language and data. Our dataset consists of 1400 sentences translated to Turkish from the Penn Treebank Corpus. This paper seeks to address and discuss 6 different feature extraction methods and its classification performances using C4.5, Random Forests, Rocchio, Naive Bayes, KNN, Linear and multilayer Perceptron. This paper calls into question how the described features perform on a morphologically rich language (Turkish) with several classifiers.
