Logical Intelligent Detection Algorithm of Chinese Language Articles Based on Text Mining

Zihui Zheng
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Abstract

With the advent of the big data era and the rapid development of the Internet industry, the information processing technology of text mining has become an indispensable role in natural language processing. In our daily life, many things cannot be separated from natural language processing technology, such as machine translation, intelligent response, and semantic search. At the same time, with the development of artificial intelligence, text mining technology has gradually developed into a research hotspot. There are many ways to realize text mining. This paper mainly describes the realization of web text mining and the realization of text structure algorithm based on HTML through a variety of methods to compare the specific clustering time of web text mining. Through this comparison, we can also get which web mining is the most efficient. The use of WebKB datasets for many times in experimental comparison also reflects that Web text mining for the Chinese language logic intelligent detection algorithm provides a basis.
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基于文本挖掘的中文文章逻辑智能检测算法
随着大数据时代的到来和互联网产业的快速发展,文本挖掘这一信息处理技术已经成为自然语言处理中不可或缺的重要组成部分。在我们的日常生活中,很多事情都离不开自然语言处理技术,比如机器翻译、智能响应、语义搜索等。同时,随着人工智能的发展,文本挖掘技术也逐渐发展成为一个研究热点。实现文本挖掘的方法有很多。本文主要介绍了web文本挖掘的实现和基于HTML的文本结构算法的实现,通过多种方法对web文本挖掘的具体聚类时间进行比较。通过这种比较,我们也可以得出哪种web挖掘效率最高。多次使用WebKB数据集进行实验对比也反映了Web文本挖掘为中文语言逻辑智能检测算法提供了依据。
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