SUBSYSTEM FOR AUTOMATIC TEXT ANNOTATION BASED ON MACHINE LEARNING METHODS
Abstract
This paper considers the problem of automatic text annotation. The formulation of the problem
is considered. The relevance and importance of developing effective methods and software
systems for solving the problem of automatic text summarization in modern information systems is
substantiated. Definitions of the concepts “data” and knowledge are given.” A list of tasks related
to the Data Mining direction is described. The Text Mining problem and existing methods for solving
it are described in detail. The problem of summarizing texts is considered. The main stages of
solving the summation problem are highlighted. The main methods of automatic text processing
are described, their advantages and disadvantages are highlighted. Abstractive summarization
and extractive summarization methods are discussed in detail. A comparative analysis of the effectiveness
of various abstracting and quasi-abstracting methods has been carried out, their key advantages
and disadvantages have been highlighted. A brief description of the encoder-decoder
architecture is given from the point of view of using this architecture in the developed algorithm for automatic text summarization. A description of the model of recurrent neural networks is given,
the advantages and disadvantages of such models are noted. The architecture of a recurrent
neural network is considered in relation to solving the problem of automatic text summarization. A
description of the modified model of a recurrent neural network – a neural network with long
short-term memory – is given. A description of the proposed automatic abstracting algorithm and
the settings of its main parameters are given. A description of the developed automatic abstracting
software subsystem is given. Computer modeling is performed and the results obtained during
computational experiments are presented. The quality of the solutions obtained was assessed. The
optimal parameters of the developed software system are determined. Directions for continuing
research are formulated.
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