确定频率字典中用于文本分类的关键字范围的方法

Olesia Barkovska, Dmytro Mohylevskyi, Yuliia Ivanenko, Dmytro Rosinskiy
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引用次数: 0

摘要

本文研究了按特征对文集文本文献进行分类的实际问题,用于对新闻、评论进行分类,确定文本的情感基调,以及形成科学、学术和研究作品的目录。本文提出了一种确定文档中重要词的方法,以便在分类过程中作为特征向量进一步使用。在工作过程中,对作者关键词进行识别,构建部分词典,并分析作者关键词与基于TF方法的频率词典有序词表之间的相关性,其中也包括作者关键词。确定重要词的范围和百分比,可以在形成专题目录时对科研论文进行进一步分类,即使在没有可用于分类的作者关键词列表的情况下也是如此。结果表明,使用整个频率字典输入范围的词是冗余的,导致分类时间更长。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
WAYS TO DETERMINE THE RANGE OF KEYWORDS IN A FREQUENCY DICTIONARY FOR TEXT CLASSIFICATION
The paper is devoted to the actual problem of classifying textual documents of the collection by characteristic features, which is used for classifying news, reviews, determining the emotional tone of the text, as well as for forming catalogs of scientific, academic and research works. The paper proposes an approach for determining the significant words of a document for their further use as a feature vector in the classification process. In the course of the work, the author's keywords were identified, a partial dictionary was built, and the correlation between the author's keywords and the list of ordered words of the frequency dictionary based on the TF method, which also includes the author’s keywords, was analyzed. The determination of the range and percentage of significant words allows for further classification of scientific and research papers when forming thematic catalogs even in the absence of a list of author's keywords that can be used for classification. The results show that the use of the entire input range of frequency dictionary words is redundant and leads to a longer classification time.
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