Multiclass Document Classifier using BERT

Shruti A. Gadewar, Prof. P. H. Pawar
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Abstract

With the rapid expansion of the internet, there has been an exponential surge in data volume, encompassing a myriad of documents laden with diverse types of information. This vast expanse includes structured and unstructured data, ranging from big data sets to formatted text and unformatted content. However, this abundance of unstructured data poses significant challenges in terms of effective management. Manual classification of this burgeoning data landscape is impractical, necessitating automated solutions. In this paper, we propose leveraging advanced machine learning techniques, particularly the BERT model, to classify documents based on contextual understanding, offering a more efficient and accurate approach to handling the data deluge.
使用 BERT 的多类文档分类器
随着互联网的飞速发展,数据量呈指数级激增,其中包括无数的文档和各种类型的信息。这些庞大的数据既有结构化数据,也有非结构化数据,既有大型数据集,也有格式化文本和非格式化内容。然而,大量的非结构化数据给有效管理带来了巨大挑战。对这一快速增长的数据环境进行人工分类是不切实际的,因此需要自动化解决方案。在本文中,我们建议利用先进的机器学习技术,特别是 BERT 模型,根据对上下文的理解对文档进行分类,从而提供一种更高效、更准确的方法来处理数据洪流。
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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