基于专业背景知识的关键词提取算法的研究与实现

Xuekun Zhang, Jing An, Wen Liu
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引用次数: 2

摘要

随着互联网的发展,数据信息正以爆发式的速度增长。随着大数据时代的到来,信息的社会价值只能通过人们的利用来体现。在海量的数据中,关键词作为比较简明扼要的文档,其可以提供高效的信息管理方法。关键字提取技术(KET)可以帮助人们准确、快速地获取数据信息,因此在信息管理系统中得到了广泛的应用。本文根据近年来对关键词提取方法的研究,对经典的TF-IDF算法和TextRank算法进行了研究,在TF-IDF算法的思想基础上对TextRank算法进行了改进和创新,设计了TextRank改进算法的过程,并通过实验证明了改进后的TextRank算法提取关键词的准确性。
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Research and implementation of keyword extraction algorithm based on professional background knowledge
With the development of Internet, Data information is growing at an explosive rate. With the era of big data coming, information social value can only be reflected by people's utilization. In the vast amounts of data, keywords as relatively concise summary of the documentation, its can provide efficient information management methods. Keyword extraction technology (KET)can help people get the data information accurately and quickly, so KET is widely used in the information management system. According to the study of keyword extraction method recent years, the classic TF — IDF algorithm and TextRank algorithm were studied in this paper, TextRank algorithm improved and innovated based on the idea of TF-IDF algorithm, the process of TextRank improved algorithms designed and experiments proved the accuracy of the keyword extraction of the improved TextRank algorithm.
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