Automated methods of coherence evaluation of Ukrainian texts using machine learning techniques

A.A. Kramov, S.D. Pogorilyy

Abstract


The main methods of coherence evaluation of texts with the usage of different machine learning techniques have been analyzed. The principles of methods with the usage of recurrent and convolutional neural networks have been described in details. The advantages of a semantic similarity graph method have been considered. Other approaches to perform the vector representation of sentences for the estimation of semantic similarity between the elements of a text have been suggested to use.  The experimental examination of methods has been performed on the set of Ukrainian scientific articles. The training of recurrent and convolutional networks with the usage of early stopping has been performed. The accuracy of the solving of document discrimination and insertion tasks has been calculated. The comparative analysis of the results obtained has been performed.

Problems in programming 2020; 2-3: 295-303


Keywords


coherence of a text; recurrent neural network; convolutional neural network; semantic similarity graph; semantic representation of sentences; document discrimination task; insertion task

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References


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