"Lesotho Leaps Forward: A Chronicle of Transformative Developments"
Text Mining Framework
1. RDIET: Recognition and Discovery of Information from
Extracted TEXT
Prakhyath Rai
III Semester M.Tech. {CSE}
NMAMIT, Nitte
Under the Guidance of:
Mr. Vijay Murari
Asst. Professor, Dept. of CSE
NMAMIT, Nitte
2. Outline
Introduction
I/O Model for Text Mining
Literature Survey
Problem Statement
Architecture Diagram
Filtering Process
Screenshots
References
CONTENTS
Depatment of CSE NMAMIT, Nitte
3. Introduction
Text Mining is a Discovery
Text Mining is used to extract relevant information or
knowledge or pattern from different sources that are in
unstructured form
• Handles semi-structured or
Unstructured dataText Mining
• Handles structured data
Data Mining
Depatment of CSE NMAMIT, Nitte
4. Introduction Cont.
Extract and discover knowledge hidden in text
automatically
Aid domain experts by automatically:
identifying concepts
extracting facts/relations
discovering implicit links
generating hypotheses
Depatment of CSE NMAMIT, Nitte
5. Input-Output Model for Text Mining
Input
Text Mining
Technique
Output
Patterns
Connections
Trends
Documents
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7. Literature Survey Cont.
Information Extraction (IE)
Knowledge Discovery from Databases (KDD)
KDT (Knowledge Discovery from Text)
High Specificity i.e. Low frequency Problem
Misinterpretations with low frequency pattern
Depatment of CSE NMAMIT, Nitte
8. Problem Statement
RDIET {Recognition and Discovery of Information
from Extracted Text} demonstrates a framework for
text mining
RDIET
IE
KDT
Standard Rule
Induction
Depatment of CSE NMAMIT, Nitte
14. References
[1] Ning Zhong, Yuefeng Li and T. Grance, “Effective Pattern Discovery for Text Mining,”
IEEE Transactions on Knowledge and Data Engineering, Vol. 24, No. 1, January 2012.
[2] Sangno Lee, Jeff Baker and Jaeki, “An Emperical Comparison of Four Text Mining
Methods”, Proceedings of the 43rd Hawaii International Conferences on System Sciences,
2010.
[3] Gary King, Patrick Lam and Margaret E Aroberts, “Computer-Assisted Keyword and
Documents from Unstructured Text”, 2014.
[4] Deepak Agnihotri, Kesari Verma and Priyanka Tripathi, “Pattern and Cluster Mining on
Text Data”, Fourth International Conferences on Communication Systems and Network
Technologies, 2014.
[5] Robert Moro and Maria Bielikova, “Personalized Text Summarization Based on
Important Terms Identification”, 23rd International Workshop on Database and Expert
Systems Applications, 2012.
Depatment of CSE NMAMIT, Nitte
15. Reference Cont.
[6] M Sukanya and S Biruntha, “Techniques on Text Mining”, IEEE Conference
on Advanced Communication Control and Computing Technologies, 2012.
[7] Christina Feilmayr, “Text Mining-Supported Information Extraction”, 22nd
International Workshop on Database and Expert Systems Applications, 2012.
[8] hadoop.intel.com, intel.com/bigdata, intel.com/microservers, “Extract,
Transform, and Load Big Data with Apache Hadoop”, http://hadoop.intel.com
[9] Raymond J Mooney and Un Yong Nahm, “ Text Mining with Information
Extraction”, Proceedings of the 4th International MIDP Colloquium, pages 141-
160, Van Schaik Pub., South Africa, 2005.
[10] R Baeza-Yates and B Ribeiro-Neto. “Modern Information Retrieval”, ACM
Press, New York, 1999.
Depatment of CSE NMAMIT, Nitte