基于关键词的文本分类模型研究与应用

Kuncheng Li, Chunmei Fan
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引用次数: 0

摘要

文本分类是根据文本的内容为文本分配预定义标签的过程。它是自然语言处理(NLP)的一个常见任务。传统的文本分类方法是机器学习,其效果很大程度上取决于训练数据集的数量和准确性,而训练数据集在大多数情况下很难获得。构建训练数据集的工作效率低且成本高。这激发了一个研究词打破这一障碍,用关键字代替的方法来完成文本分类。在这项工作中,我们探索了标签-关键字对(每个标签都有一组关键字)的使用,即使没有训练数据集,也可以自动将文本文档分配到一个或多个类别,从而获得与手动分类文本的系统相当的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research and Application on Text Classification Model Based on Keywords
Text classification is the process of assigning predefined labels to text by its content. It is a common task of Natural Language Processing (NLP). The traditional way for text classification is machine learning and its effect is greatly depended on the amount and accuracy of the training data set which is difficult to obtain in most cases. The job of building the training data set is inefficient and expensive [1]. This has motivated a research word to break this barrier, with a method using keywords instead to complete text classification. In this work, we explore the usage of label-keywords pairs (each label has a set of keywords) for assigning text documents to one or more categories automatically even without the training data set, obtaining results comparable to those systems that classify the text manually.
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