A Stochastic Finite-State Morphological Parser for Turkish

Hasim Sak, Tunga Güngör, Murat Saraclar

Abstract: This paper presents the first stochastic finite-state morphological parser for Turkish. The non-probabilistic parser is a standard finite-state transducer implementation of two-level morphology formalism. A disambiguated text corpus of 200 million words is used to stochastize the morphotactics transducer, then it is composed with the morphophonemics transducer to get a stochastic morphological parser. We present two applications to evaluate the effectiveness of the stochastic parser; spelling correction and morphology-based language modeling for speech recognition.