Mühendislik Fakültesi / Faculty of Engineering Bilgisayar Mühendisliği / Computer EngineeringAdalı, EşrefTantuğ, Ahmet CüneydİLGEN, BAHAR2018-10-252018-10-252012-06-131543-9259https://doi.org/10.1109/INES.2012.6249891https://hdl.handle.net/11413/2935Word 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 order to obtain better Turkish WSD systems. A lexical sample dataset of highly polysemous nouns and verbs has been prepared as the initial step of the work. Several supervised learning algorithms have been tested on this data by supplying different feature sets to select the best performing features for both nouns and verbs in Turkish. Also, we investigated the impact of several collocational features of polysemous words and evaluated the performance of several supervised machine learning algorithms.en-USWord Sense Disambiguationfeature selectionmachine learningconferenceObject