A Novel Text Classification Approach Based on Word2vec and TextRank Keyword Extraction

Song, Shouyou; Wang, Zhongru; Xu, Shuai; Ni, Shice; Xiao, Jia

doi:10.1109/dsc.2019.00087

Cited by 5 publications

(1 citation statement)

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“…Second, TextRank can find the most informative items from the text by dividing the text into several components, such as words and sentences, and building a text graph between the components. Third, TextRank has been widely applied to real applications, including identifying web content credibility [7], extract a multi-document summary [8] and text classification [9], and no learning or training process is required previously.…”

Section: Introductionmentioning

confidence: 99%

An Empirical Study of TextRank for Keyword Extraction

Zhang

Yue

et al. 2020

IEEE Access

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As a typical keyword extraction technology, TextRank has been used in a wide variety of commercial applications, including text classification, information retrieval and clustering. In these applications, the parameters of TextRank, including the co-occurrence window size, iteration number and decay factor, are set roughly, which might affect the effectiveness of returned results. In this work, we conduct an empirical study on TextRank, towards finding optimal parameter settings for keyword extraction. The experiments are done in Hulth2003 and Krapivin2009 datasets, which are two real datasets. We first remove the stop word by an open published English stop word list XPO6. And then, we extract the word stems by Porter Stemmer. Porter Stemmer is a tool which can find the stems of words with multiple variants, discard redundant information, strengthen the filtering effect, and extract the effective features of the text fully. We carry out extensive experiments to evaluate the effects of the parameters to keywords extraction, and evaluate the effectiveness of corresponding results by Precision, Recall and Accuracy. Experimental results show that TextRank shows the best performance when setting co-occurrence window size w = 3, iteration number t = 20, decay factor c = 0.9 and rank k = 10 respectively, and the results are independent of the text length.

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Section: Introductionmentioning

confidence: 99%