Esref Adali

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The K-means algorithm is quite sensitive to the cluster centers selected initially and can perform different clusterings depending on these initialization conditions. Within the scope of this study, a new method based on the Fuzzy ART algorithm which is called Improved Fuzzy ART (IFART) is used in the determination of initial cluster centers. By using(More)
In this work, we present a MT system from Turkmen to Turkish. Our system exploits the similarity of the languages by using a modified version of direct translation method. However, the complex inflectional and derivational morphology of the Turkic languages necessitate special treatment for word-byword translation model. We also employ morphology-aware(More)
This paper describes the differences between Uyghur (spoken in Sin Kiang, China) and Turkish Grammar on the sentence level. There are not many researches about natural language processing on Turkic languages except than Turkish. Uyghur language is one of the old and rich language in the Turkic language family. Even though both of these languages belong to(More)
This paper presents the results of main part-of-speech tagging of Turkish sentences using Conditional Random Fields (CRFs). Although CRFs are applied to many different languages for part-of-speech (POS) tagging, Turkish poses interesting challenges to be modeled with them. The challenges include issues related to the statistical model of the problem as well(More)
This paper describes the implementation of a two-level morphological analyzer for the Turkmen Language. Like all Turkic languages, the Turk-men Language is an agglutinative language that has productive inflectional and derivational suffixes. In this work, we implemented a finite-state two-level morphological analyzer for Turkmen Language by using Xerox(More)
Word Sense Disambiguation (WSD) is the task of choosing the most appropriate sense of a word having multiple senses in a given context. Collocational features acquired from the words in neighborship with the ambiguous word are one of the important knowledge sources in this area. This paper explores the effective sets of collocational features in Turkish in(More)
Word Sense Disambiguation (WSD) has become even more important research area in recent years with the widespread usage of Natural Language Processing (NLP) applications. WSD task has two variants: “Lexical Sample” and “All Words” approaches. Lexical Sample approach disambiguates the occurrences of a small sample of target words(More)