Text Knowledge Mining: An Alternative to Text Data Mining

Sánchez, Daniel; Martín-Bautista, María J.; Blanco, Ignacio J.; Torre, C. Justicia de la

doi:10.1109/icdmw.2008.57

Cited by 28 publications

(20 citation statements)

References 11 publications

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“…Text mining is a method for the detection of new, nontrivial and potentially useful knowledge [1,2,3]. Text mining has some common features with, and uses many techniques from the conventional data mining [4].…”

Section: Introductionmentioning

confidence: 99%

“…But, according to much of the literature surveyed in the domain, text mining is dissimilar to the rest of data mining because of the unstructured nature of the text [1,4,7,8,9]. It is explained in [3] that in fact text has a structure, but that it is very complicated, making the process of analysis non-trivial. Another reason for this opposition is that 80% of all information is textual [1,3,10,11].…”

Section: Introductionmentioning

confidence: 99%

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Application of formal grammar in text mining and construction of an ontology

Kanev

Cunningham

Terekhov

2017

2017 Internet Technologies and Applications (ITA)

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This work describes an investigation of formal grammar with application to text mining. It is an important area since text is the most widespread type of data and it contains a lot of potentially useful information. Unstructured nature of text requires other methods for its processing, in contrast to other types of data mining. In this work, the authors propose an original approach to text mining by making a parse tree for each sentence using regular grammar and creating an ontology and provide a demonstration of this system being implemented in a constrained scenario. This ontology can be used for different tasks, ranging from expert systems to automatic machine translation. The ontology is a network consisting of concepts linked by relations. The authors developed a new system to implement proposed approach working in different languages.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of formal grammar in text mining and construction of an ontology

Kanev

Cunningham

Terekhov

2017

2017 Internet Technologies and Applications (ITA)

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“…[15] Thus for large document collections, both row and column dimensions of the matrix are quite large as well as sparse. According to the size of the VSM the most critical problem for text classification is the high dimensionality.…”

Section: Introductionmentioning

confidence: 99%

Text Document Classification basedon Least Square Support Vector Machines with Singular Value Decomposition

M.Ramakrishna¹,

J.V.R²,

Prasad³

2011

IJCA

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Due to rapid growth of on-line information, text classification has become one of key technique for handling and organizing text data. One of the reasons to build taxonomy of documents is to make it easier to find relevant documents, content filtering and topic tracking.LS-SVM is the classifier, used in this paper for efficient classification of text documents. Text data is normally highdimensional characteristic, to reduce the high-dimensionality also possible with SVM.In this paper we are improving classification accuracy and dimensionality reduction of a large text data by Least Square Support Vector Machines along with Singular Value Decomposition.

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“…With the use of bit parallelism in String Matching algorithm speed of matching is improved up to certain level. String matching [2] algorithms are used in most of the real world applications where pattern extraction is required like as Intrusion Detection system [3][4], Plagiarism detection [5], Data Mining [6] and Bioinformatics [7]. Bit parallel algorithms are faster than the other benchmark character based algorithms like as KMP [4] [8], BM [9][10], BMH [11] [12], BMHS [13], BMHS2 [14], BMI [15], Improved BMHS [16], Cmmentz Walter [17][18], Wu Manber [19] [20] and Aho-Corasick [21] [22] etc.…”

Section: Introductionmentioning

confidence: 99%

Bit Parallel String Matching Algorithms: A Survey

Gupta¹,

Rasool²

2014

IJCA

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The intrinsic parallelism in bit operations like AND/OR inside a computer word is known as bit parallelism. Since 1992, this bit parallelism is directly used in string matching for matching efficiency improvement. Some of the popular bit parallel string matching algorithms Shift OR, Shift OR with Q-Gram, BNDM, TNDM, SBNDM, LBNDM, FBNDM, BNDMq, and Multiple pattern BNDM. This paper discusses the working of various bit parallel string matching algorithms with example. Here we present how bit parallelism is useful for efficiency improvement in various algorithms.

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Text Knowledge Mining: An Alternative to Text Data Mining

Cited by 28 publications

References 11 publications

Application of formal grammar in text mining and construction of an ontology

Application of formal grammar in text mining and construction of an ontology

Text Document Classification basedon Least Square Support Vector Machines with Singular Value Decomposition

Bit Parallel String Matching Algorithms: A Survey

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