Probability of Semantic Similarity and N-grams Pattern Learning for Data Classification

Probability of Semantic Similarity and N-grams Pattern Learning for Data Classification

V Vineeth Kumar

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Dr. N Satyanarayana.

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Jawaharlal Nehru Technological University, Hyderabad

Probability of Semantic Similarity and N-grams Pattern Learning for Data Classification

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Abstract

Semantic learning is an important mechanism for the document classification, but most classification approaches are only considered the content and words distribution. Traditional classification algorithms cannot accurately represent the meaning of a document because it does not take into account semantic relations between words. In this paper, we present an approach for classification of documents by incorporating two similarity computing score method. First, a semantic similarity method which computes the probable similarity based on the Bayes’ method and second, n-grams pairs based on the frequent terms probability similarity score. Since, both semantic and Ngrams pairs can play important roles in a separated views for the classification of the document, we design a semantic similarity learning (SSL) algorithm to improves the performance of document classification for a huge quantity of unclassified documents.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

V Vineeth Kumar. 2017. \u201cProbability of Semantic Similarity and N-grams Pattern Learning for Data Classification\u201d. Global Journal of Computer Science and Technology - H: Information & Technology GJCST-H Volume 17 (GJCST Volume 17 Issue H2).

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GJCST Volume 17 Issue H2

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

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Classification

GJCST-H Classification G.3 I.5

I.5.2

Submission ReceivedDecember 6, 2016
Peer Review Double Blind
Handling Editor
Accepted January 5, 2017
Published January 15, 2017

Version of record

v1.2

Issue date

May 24, 2017

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Article Matrices

Total Score: 107

Country: India

Subject: Global Journal of Computer Science and Technology - H: Information & Technology

Authors: V Vineeth Kumar, Dr. N Satyanarayana. (PhD/Dr. count: 1)

View Count (all-time): 306

Total Views (Real + Logic): 6580

Total Downloads (simulated): 1687

Publish Date: 2017 05, Wed

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Total Views: 6580

Total Downloads: 1687

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