A hybrid method to segment words

Dai, Yubiao; Ren, Xueli

doi:10.1109/icalip.2012.6376786

Cited by 1 publication

(1 citation statement)

References 4 publications

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“…Since accuracy of reverse matching is slightly higher than the forward matching and less ambiguity, reverse maximum matching is chosen in this paper. Reverse Maximum Matching (also known as RMM) ABC is a string of the text, if C∈W，BC∈W and ABC  W（W is a word in the dictionary）, then it is segmented to word A and BC [6]. The reverse maximum matching method is used to segment words of the text based on the common dictionary.…”

Section: Reverse Matchmentioning

confidence: 99%

A New Method of Text Feature Selection for Knowledge Discovery

Zhang¹,

Liu²,

An³

et al. 2015

Advances in Intelligent Systems Research

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a hearingme@yeah.net Keywords: knowledge discovery; data mining; information model Abstract. In the paper, we considered the problem to model the text document for knowledge discovery. It is urgent problem to find valuable information knowledge in the mass text documents. Data mining based on eigenvector is widely used to mine association between information of mass documents as much as possible for knowledge discovery. However, traditional information model methods for text are hard to achieve the feature vector for some special knowledge discovery task. This paper proposed a new feature selection method of text for knowledge discovery, which is useful to find the valuable words of the text for the special knowledge discovery. In addition, the dimension of the feature vector has been reduced by proposed method, which is of great help to improve the efficiency of data mining effectively.

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Section: Reverse Matchmentioning

confidence: 99%