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 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)
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)
We present an approach to MT between Tur-kic languages and present results from an implementation of a MT system from Turk-men to Turkish. Our approach relies on ambiguous lexical and morphological transfer augmented with target side rule-based repairs and rescoring with statistical language models.