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Yayın Kural bazlı otomatik haber etiketleme(IEEE, 2017-06-27) Özenç, Berke; Solak, ErcanBu çalışmada , genel ağ kaynaklarından haber toplayan ve topladığı bu haberleri otomatik olarak etiketleyen kural tabanlı bir uygulama yapılmıştır. Çalışmanın alt amacı hangi özelliklerin etiket belirleme işine daha uygun olduğunu ölçmektir. Elle etiketlenmiş 100 haber üzerinde her bir kuralın başarısı oranı ölçülmüştür.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.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.
