旁遮普语文献分类的向量评价方法

Mehak Katnoria, Varinderpal Singh, Rajiv Kumar
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引用次数: 5

摘要

大多数信息以文本形式存储,在文本挖掘应用程序中,以数字形式管理大量文档是至关重要的。文本挖掘是一个根据用户的查询从文本文档中提取隐藏的、有用的信息的字段。文本分类是文本挖掘的重要组成部分。文本分类,也称为文本分类或主题定位,被定义为在预定义的类别下对文本文档进行分类。虽然旁遮普语文本分类是一个很有前途的领域,但与英语文本分类相比,做的工作并不多。本文提出了一种使用分类后的旁遮普语文档语料库,通过计算被测文档词的权重来确定文档关键词,并与语料库中的关键词进行比较,从而确定被测文档的最佳类别的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Punjabi document classification using vector evaluation method
Most information is stored as text, managing a vast amount of documents in digital forms is vital in text mining applications. Text Mining is a field that extracts hidden, useful information from the text document according to user's query. Text Categorization is the most important part of Text Mining. Text Categorization, also known as Text Classification or topic spotting, is defined as a classification of text documents under predefined categories. Although Punjabi text categorization is a promising field, not much work has been done as compared to English text categorization. This paper proposes a method which uses a categorized Punjabi documents corpus, and then the weights of the tested document's words are calculated to determine the document keywords which will be compared with the keywords of the corpus to determine the tested document's best category.
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