Methods of Intellectual Text Analysis

Author:

Demidovich I. M.1ORCID

Affiliation:

1. Ukrainian State University of Science and Technologies

Abstract

Purpose. Natural language text processing techniques are used to solve a wide range of tasks. One of the most difficult tasks when working with natural language texts for different languages is to find certain indicators for further determining its authorship. The problem is still relevant due to the lack of a unified tool or method for working with texts in different languages. Working with texts in Ukrainian requires taking into account its peculiarities of word and sentence construction to obtain the best result. The main purpose of this article is to analyze the existing methods of text processing, their features and effectiveness in working with texts of different languages. Methodology. Natural language text processing methods are systematized by type and format, according to the tools and approaches used. For each method, its features, effectiveness, scope, and limitations are considered. The means of system analysis were used to form the final characterization of the method, taking into account its purpose and capabilities. Findings. The study of methods has revealed the following ones used for the intellectual analysis of texts in different languages, their scope, effectiveness in working with different languages, strengths and weaknesses. This will make it possible to choose an effective toolkit for working with Ukrainian texts. It has been established that intelligent text processing is a complex task that requires an individual approach to each language to take into account its peculiarities and obtain the best result. Originality. The basis for choosing an effective method for working with Ukrainian-language texts is formed, the existing methods of intellectual text processing, their application features, capabilities and efficiency in working with texts of different languages are analyzed and systematized. Practical value. The work allowed us to identify the most promising, effective and appropriate methods of intellectual analysis of natural language texts in order to use them for processing Ukrainian-language texts in the future.

Publisher

Ukrainian State University of Science and Technologies

Reference65 articles.

1. Slavic experience of compiling a frequency dictionary of writer’s language.;Buk;Problems of slavonic studies,2011

2. Funktsionalnyy styl khudozhnoho movlennya;Voitenko;Naukovì zapiski Nacìonalʹnogo unìversitetu «Ostrozʹka akademìâ». Serìâ Fìlologìčna,2012

3. Perebyynis, V. S. (2002). Statystychni metody dlya linhvistiv: navchalnyy posibnyk. Vinnytsya: Nova knyha. (in Ukrainian)

4. A Naïve-Bayes classifier for damage detection in engineering materials;Addin;Materials & Design,2007

5. Aggarwal, C. C. (2018). Machine Learning for Text (pp. 1-6). Springer International Publishing. DOI: https://doi.org/10.1007/978-3-319-73531-3 (in English)

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3