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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-by-word translation model. We also employ morphology-aware(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)
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)
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)
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 a statistical lexical ambiguity resolution method in direct transfer machine translation models in which the target language is Turkish. Since direct transfer MT models do not have full syntactic information, most of the lexical ambiguity resolution methods are not very helpful. Our disambiguation model is based on statistical language(More)