A Frame Work for Text Mining using Learned Information Extraction System

A Frame Work for Text Mining using Learned Information Extraction System

Sathish Kuppani

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M.Vasavi

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Sri Venkateswara University

A Frame Work for Text Mining using Learned Information Extraction System

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Abstract

Text mining is a very exciting research area as it tries to discover knowledge from unstructured texts. These texts can be found on a computer desktop, intranets and the internet. The aim of this paper is to give an overview of text mining in the contexts of its techniques, application domains and the most challenging issue. The Learned Information Extraction (LIE) is about locating specific items in natural-language documents. This paper presents a framework for text mining, called DTEX (Discovery Text Extraction), using a learned information extraction system to transform text into more structured data which is then mined for interesting relationships. The initial version of DTEX integrates an LIE module acquired by an LIE learning system, and a standard rule induction module. In addition, rules mined from a database extracted from a corpus of texts are used to predict additional information to extract from future documents, thereby improving the recall of the underlying extraction system. Applying these techniques best results are presented to a corpus of computer job announcement postings from an Internet newsgroup.

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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.

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How to Cite This Article

Sathish Kuppani. 2016. \u201cA Frame Work for Text Mining using Learned Information Extraction System\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 16 (GJCST Volume 16 Issue C3): .

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GJCST Volume 16 Issue C3
Pg. 41- 50

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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Classification

GJCST-C Classification: I.2.4, D.3.3

Submission ReceivedDecember 6, 2015
Peer Review Double Blind
Handling Editor
Accepted January 3, 2016
Published January 15, 2016

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v1.2

Issue date

July 1, 2016

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Article Matrices

Total Score: 102

Country: India

Subject: Global Journal of Computer Science and Technology - C: Software & Data Engineering

Authors: M.Vasavi, Sathish Kuppani (PhD/Dr. count: 0)

View Count (all-time): 276

Total Views (Real + Logic): 7450

Total Downloads (simulated): 1862

Publish Date: 2016 07, Fri

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Total Views: 7450

Total Downloads: 1862

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