Although our experiments are performed on Turkish language, the same model is also applicable to other agglutinative languages such as Hungarian and Finnish. We generated approximately 1 million word forms by using only a few thousand Turkish stems with an accuracy of 82.36%, which will help to reduce the out-of-vocabulary size in other NLP applications. mathematician Andrey Markov used finite-state process- automata theory in each level of natural language pro- cessing by surveying some of the significant. noun, verb, or adjective) and suffixes are clustered based on their allomorphic features. Stems are clustered based on their parts-of-speech (i.e. Each FSA state corresponds to either a stem or a suffix category. We extracted orthographic features by capturing phonological operations that are applied to words whenever a suffix is added. Our model uses finite-state automata (FSA) to incorporate orthographic features and morphotactic rules. We aimed to expand the lexicon by using a morphological segmentation system by reversing the segmentation task into a generation task. This article presents a model for Turkish lexicon expansion. Graphical representation of DFA Finite automata lend themselves readily to graphical interpretation. Therefore, sparsity becomes an issue in many Turkish natural language processing (NLP) applications. Theorem: A language is accepted by a DFA if and only if it is a regular language (i.e., has a regular expression). A Turkish verb can have thousands of different word forms. Abstract Turkish is an agglutinative language with rich morphology.

Finite state automata natural langauge