On Text Understanding Modeling by Means of Artificial Intelligence Systems

T. V. Kruzhilina
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Abstract

Purpose of reseach is to conduct a comparative analysis of texts generated by an artificial intelligence system in the process of processing source text in natural language and counter texts that are the result of human understanding of the source literary text. Methods. To achieve the goal and objectives of the study, the author used an experimental technique to conduct a comparative analysis of the denotational structures of counter texts. Participants in the experiment (7 4th year students of the Faculty of Additional Education “Translator in the field of professional communications”, 3 associate professors of the Department of Foreign Languages of South- West State University) assessed the success of recreating the semantic structure of the text of 10 counter texts of different nature - generated by AI and humans. Results. The results of the experiment indicate that the completeness of the semantic content of the generated text does not depend on the structure of the source text. Modern methods of semantic text processing by an AI system make it possible to obtain the output of full-fledged text works created taking into account the rules and norms of natural language.AI systems successfully recreate the denotational structure of the text and reconstruct the syntactic structure. Conclusion. Access to large databases allows you to train a neural network on large text corpora, which results in an increase in the accuracy and variability of the lexical units and constructs used. The accuracy of conveying the semantic content of the text varies. It depends on the degree of text compression - the higher it is, the less accuracy can be, because the neural network is unable to classify denotational connections for relevance to the underlying meaning. The degree of accuracy in conveying semantic content is determined by the success / failure of understanding the deep hidden meaning, which is determined by the understanding of the linguistic and extralinguistic context. The ability to recognize the situation model recreated in the source text is the key to understanding the hidden meaning. The AI system can recreate the surface denotational structure of the text quite correctly and accurately, but is not able to construct a model of the situation at this stage of development.
论通过人工智能系统进行文本理解建模
研究目的是对人工智能系统在处理自然语言源文本过程中生成的文本和人类对源文学文本的理解所产生的反文本进行比较分析。研究方法。为了实现研究的目的和目标,作者使用实验技术对反文本的指称结构进行了比较分析。实验参与者(7 名补充教育学院 "专业交流领域翻译 "专业的四年级学生,3 名西南州立大学外语系副教授)对人工智能和人类生成的 10 篇不同性质的反义词文本的语义结构再现成功率进行了评估。结果。实验结果表明,生成文本语义内容的完整性并不取决于源文本的结构。人工智能系统对文本进行语义处理的现代方法使我们有可能在考虑自然语言规则和规范的基础上获得完整文本作品的输出。结论通过访问大型数据库,可以在大型文本语料库中训练神经网络,从而提高所用词汇单位和结构的准确性和可变性。传达文本语义内容的准确性各不相同。它取决于文本的压缩程度--压缩程度越高,准确性就越低,因为神经网络无法对指称连接进行分类,以确定其与基本含义的相关性。传达语义内容的准确程度取决于对深层隐含意义理解的成败,而对深层隐含意义的理解则取决于对语言和语言外语境的理解。能否识别源文本中再现的情境模型是理解隐含意义的关键。人工智能系统可以相当正确和准确地再现文本的表层指称结构,但在这一发展阶段还无法构建情境模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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