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Impact of Morphological Preprocessing of Training Corpus on Quality of Russian Distributional Semantic Models
Language Theory and Computational Linguistics
This paper examines the impact of morphological preprocessing on quality of distributional language models when applied to text classification tasks, covering both theoretical grounds of distributional language modelling and morphological preprocessing, and their practical applications. Experiments presented in this paper include three kinds of morphological preprocessing (stemming, lemmatization, part-of-speech-tagging) as well as three types of distributional modelling algorithms (word2vec skip-gram, FastText skip-gram and GloVe). Using four different classification approaches, including neural network-based, we show how preprocessing affects the results and describe the cases where the effect is positive, and the cases where preprocessing should be avoided.