Sense Differentiation of Texts as a Component of Neural Network Modelling

Abstract

The article argues that the most productive for linguistic research at the present stage is the use of Artificial Neural Networks (ANNs) due to their productivity, representativeness, etc. It is emphasized that the basis for such use should be sense differentiation, thanks to which linguists can optimize the search, analysis, etc. of data for their research. In particular, taking into account semantic, morphological, syntactic, etc. features will allow the production of more reliable, fundamental results in various tasks of Natural Language Processing (NLP). The author emphasizes that the above will result in a qualitative leap in the scientific research of Ukrainian linguists, the possibility of presenting their results to world science, and further fruitful cooperation with foreign colleagues within the framework of grant programs. Thus, the semantic differentiation of texts is an integral part of the actualization of Artificial Neural Networks (ANNs) (in particular, Bidirectional Long Short-Term Memory Network (BiLSTM), Convolutional Neural Networks (CNNs), Deep Learning Networks (DLNs), Deep Neural Networks (DNNs), Graph Neural Networks (GNNs), Recurrent Neural Networks (RNNs), etc.) in modern linguistic research within the digital humanities. In the author’s opinion, the latter is produced by the focus on practical results, localization of implementation (in particular, Natural Language Processing (NLP), sentiment analysis, etc. Therefore, further study, improvement, and optimization of the existing innovative tools (in particular, neural network modelling of linguistic units) include work on more effective methods of working with context (through a combination of different types of Artificial Neural Networks (ANNs) with different layers, which is presented in the work of foreign colleagues), localization of language styles (essential in the process of fact-checking initiatives – as a milestone for validating text data), etc. without manual intervention in the above.