Language Independent POS-tagging Using Automatically Generated Markov Chains (S)

This paper proposes a method to predict word grammatical classes using automatically generated discrete-time Markov chains to model typical sentences. Such method advantage relies on the availability of input resources needed to build an efficient and effective solution to virtually any language, dialect, or domain lingo. One of the main advantages of the proposed method is its simplicity when compared to other sophisticated approaches based on Hidden Markov Models or even more complex formalisms. The proposed method is instantiated to an example and we show that the achieved efficiency and effectiveness bring advantages to traditional similar solutions.

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