A Novel Class-Center Vector Model for Text Classification Using Dependencies and a Semantic Dictionary

Automatic text classification is a research focus and core technology in information retrieval and natural language processing. Different from the traditional text classification methods (SVM, Bayesian, KNN), the class-center vector method is an important text classification method, which has the ad...
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Autor*in:	Xinhua Zhu [verfasserIn] Qingting Xu [verfasserIn] Yishan Chen [verfasserIn] Hongchao Chen [verfasserIn] Tianjun Wu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Text classification dependencies weight calculation semantic dictionary part-of-speech class-center vector

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 8(2020), Seite 24990-25000
Übergeordnetes Werk:	volume:8 ; year:2020 ; pages:24990-25000

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2019.2954106

Katalog-ID:	DOAJ056266251

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Different from the traditional text classification methods (SVM, Bayesian, KNN), the class-center vector method is an important text classification method, which has the advantages of less calculation and high efficiency. However, the traditional class-center vector method for text classification has the disadvantages that the class vector is large and sparse, and its classification accuracy is not high because of the lack of semantic information. To overcome these problems, this paper proposes a novel class-center vector model for text classification using dependencies and a semantic dictionary. We respectively use WordNet English semantic dictionary and Tongyici Cilin Chinese semantic dictionary to cluster the English or Chinese feature words in the class-center vector and to significantly reduce the dimension of class-center vector, thereby realizing a new class-center vector for text classification using dependencies and a semantic dictionary. 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callnumber-first	T - Technology
author	Xinhua Zhu
spellingShingle	Xinhua Zhu misc TK1-9971 misc Text classification misc dependencies misc weight calculation misc semantic dictionary misc part-of-speech misc class-center vector misc Electrical engineering. Electronics. Nuclear engineering A Novel Class-Center Vector Model for Text Classification Using Dependencies and a Semantic Dictionary
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topic	misc TK1-9971 misc Text classification misc dependencies misc weight calculation misc semantic dictionary misc part-of-speech misc class-center vector misc Electrical engineering. Electronics. Nuclear engineering
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title	A Novel Class-Center Vector Model for Text Classification Using Dependencies and a Semantic Dictionary
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title_auth	A Novel Class-Center Vector Model for Text Classification Using Dependencies and a Semantic Dictionary
abstract	Automatic text classification is a research focus and core technology in information retrieval and natural language processing. Different from the traditional text classification methods (SVM, Bayesian, KNN), the class-center vector method is an important text classification method, which has the advantages of less calculation and high efficiency. However, the traditional class-center vector method for text classification has the disadvantages that the class vector is large and sparse, and its classification accuracy is not high because of the lack of semantic information. To overcome these problems, this paper proposes a novel class-center vector model for text classification using dependencies and a semantic dictionary. We respectively use WordNet English semantic dictionary and Tongyici Cilin Chinese semantic dictionary to cluster the English or Chinese feature words in the class-center vector and to significantly reduce the dimension of class-center vector, thereby realizing a new class-center vector for text classification using dependencies and a semantic dictionary. Experiments show that, compared with traditional text classification algorithms, the improved class-center vector method has lower time complexity and higher accuracy on the 20Newsgroups English corpus, Fudan and Sogou Chinese corpus. This paper is an improved version of our NLPCC2019 conference paper.
abstractGer	Automatic text classification is a research focus and core technology in information retrieval and natural language processing. Different from the traditional text classification methods (SVM, Bayesian, KNN), the class-center vector method is an important text classification method, which has the advantages of less calculation and high efficiency. However, the traditional class-center vector method for text classification has the disadvantages that the class vector is large and sparse, and its classification accuracy is not high because of the lack of semantic information. To overcome these problems, this paper proposes a novel class-center vector model for text classification using dependencies and a semantic dictionary. We respectively use WordNet English semantic dictionary and Tongyici Cilin Chinese semantic dictionary to cluster the English or Chinese feature words in the class-center vector and to significantly reduce the dimension of class-center vector, thereby realizing a new class-center vector for text classification using dependencies and a semantic dictionary. Experiments show that, compared with traditional text classification algorithms, the improved class-center vector method has lower time complexity and higher accuracy on the 20Newsgroups English corpus, Fudan and Sogou Chinese corpus. This paper is an improved version of our NLPCC2019 conference paper.
abstract_unstemmed	Automatic text classification is a research focus and core technology in information retrieval and natural language processing. Different from the traditional text classification methods (SVM, Bayesian, KNN), the class-center vector method is an important text classification method, which has the advantages of less calculation and high efficiency. However, the traditional class-center vector method for text classification has the disadvantages that the class vector is large and sparse, and its classification accuracy is not high because of the lack of semantic information. To overcome these problems, this paper proposes a novel class-center vector model for text classification using dependencies and a semantic dictionary. We respectively use WordNet English semantic dictionary and Tongyici Cilin Chinese semantic dictionary to cluster the English or Chinese feature words in the class-center vector and to significantly reduce the dimension of class-center vector, thereby realizing a new class-center vector for text classification using dependencies and a semantic dictionary. Experiments show that, compared with traditional text classification algorithms, the improved class-center vector method has lower time complexity and higher accuracy on the 20Newsgroups English corpus, Fudan and Sogou Chinese corpus. This paper is an improved version of our NLPCC2019 conference paper.
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title_short	A Novel Class-Center Vector Model for Text Classification Using Dependencies and a Semantic Dictionary
url	https://doi.org/10.1109/ACCESS.2019.2954106 https://doaj.org/article/6d886cff7edb459d97500fd53dfd385b https://ieeexplore.ieee.org/document/8905993/ https://doaj.org/toc/2169-3536
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