Text classification is an important component of digital media such as natural language processing, image labeling, sentiment analysis, spam filtering, chatbots, and translators. In this work, effort was devoted to develop an in-memory processor for Bayesian text classification using memristive crossbar architecture, in which memristive switches were employed to store information required for the classification of text. The efficacy of the proposed circuit was tested on two distinct datasets consisting of a total of 55,575 texts. The circuit was found to be efficient to categorize the texts with an average accuracy of 91%. This work paves the way for hardware realization of cognitive systems using in-memory processors.

文本分类是数字媒体的重要组成部分，例如自然语言处理，图像标签，情感分析，垃圾邮件过滤，聊天机器人和翻译器。在这项工作中，我们致力于开发使用忆阻纵横式架构进行贝叶斯文本分类的内存处理器，其中忆阻开关用于存储文本分类所需的信息。在两个不同的数据集（共55,575个文本）上测试了拟议电路的效率。发现该电路可以有效地对文本进行分类，平均准确度为91％。这项工作为使用内存处理器的认知系统的硬件实现铺平了道路。