In this paper, we present a stochastic part-of-speech tagger for Turkish. The tagger is primarily developed for information retrieval purposes, but it can as well serve as a light-weight PoS tagger for other purposes. The tagger uses a well-established Hidden Markov model of the language with a closed lexicon that consists of fixed number of letters from the word endings. We have considered seven different lengths of word endings against 30 training corpus sizes. Bestcase accuracy obtained is 90.2% with 5 characters. The main contribution of this paper is to present a way of constructing a closed vocabulary for part-of-speech tagging effort that can be useful for highly inflected languages like Turkish, Finnish, Hungarian, Estonian, and Czech.
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