Enhanced intelligent text categorization using concise keyword analysis

Amir Mohammad Shahi; Biju Issac; Jashua Rajesh Modapothala

doi:10.1109/ICIMTR.2012.6236461

Enhanced intelligent text categorization using concise keyword analysis

Amir Mohammad Shahi^*, Biju Issac, Jashua Rajesh Modapothala

^*Corresponding author for this work

Computer and Information Sciences Department

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

Supervised learning is a popular approach to text classification among the research community as well as within software development industry. It enables intelligent systems to solve various text analysis problems such as document organization, spam detection and report scoring. However, the extremely difficult and time intensive process of creating a training corpus makes it inapplicable to many text classification problems. In this research, we explored the opportunities of addressing this pitfall by studying the ontological characteristics of document categories and grouping them under virtual super-categories to narrow down the search for a suitable category. Applying this method showed that classifier performance has greatly improved despite the relatively small size of the training corpus.

Original language	English
Title of host publication	ICIMTR 2012 - 2012 International Conference on Innovation, Management and Technology Research
Publisher	IEEE
Pages	574-579
Number of pages	6
ISBN (Electronic)	9781467306546
ISBN (Print)	9781467306553
DOIs	https://doi.org/10.1109/ICIMTR.2012.6236461
Publication status	Published - 12 Jul 2012
Event	2012 International Conference on Innovation, Management and Technology Research, ICIMTR 2012 - Malacca, Malaysia Duration: 21 May 2012 → 22 May 2012

Conference

Conference	2012 International Conference on Innovation, Management and Technology Research, ICIMTR 2012
Country/Territory	Malaysia
City	Malacca
Period	21/05/12 → 22/05/12

Keywords

Categorization
Corporate Sustainability Report
Feature Selection
Global Reporting Initiative
Machine Learning
Supervised Learning
Text Ontology

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ICIMTR.2012.6236461

Cite this

@inproceedings{ebb6fb2187fd4f98a60d578f6855f599,

title = "Enhanced intelligent text categorization using concise keyword analysis",

abstract = "Supervised learning is a popular approach to text classification among the research community as well as within software development industry. It enables intelligent systems to solve various text analysis problems such as document organization, spam detection and report scoring. However, the extremely difficult and time intensive process of creating a training corpus makes it inapplicable to many text classification problems. In this research, we explored the opportunities of addressing this pitfall by studying the ontological characteristics of document categories and grouping them under virtual super-categories to narrow down the search for a suitable category. Applying this method showed that classifier performance has greatly improved despite the relatively small size of the training corpus.",

keywords = "Categorization, Corporate Sustainability Report, Feature Selection, Global Reporting Initiative, Machine Learning, Supervised Learning, Text Ontology",

author = "Shahi, {Amir Mohammad} and Biju Issac and Modapothala, {Jashua Rajesh}",

year = "2012",

month = jul,

day = "12",

doi = "10.1109/ICIMTR.2012.6236461",

language = "English",

isbn = "9781467306553",

pages = "574--579",

booktitle = "ICIMTR 2012 - 2012 International Conference on Innovation, Management and Technology Research",

publisher = "IEEE",

address = "United States",

note = "2012 International Conference on Innovation, Management and Technology Research, ICIMTR 2012 ; Conference date: 21-05-2012 Through 22-05-2012",

}

Shahi, AM, Issac, B & Modapothala, JR 2012, Enhanced intelligent text categorization using concise keyword analysis. in ICIMTR 2012 - 2012 International Conference on Innovation, Management and Technology Research., 6236461, IEEE, pp. 574-579, 2012 International Conference on Innovation, Management and Technology Research, ICIMTR 2012, Malacca, Malaysia, 21/05/12. https://doi.org/10.1109/ICIMTR.2012.6236461

TY - GEN

T1 - Enhanced intelligent text categorization using concise keyword analysis

AU - Shahi, Amir Mohammad

AU - Issac, Biju

AU - Modapothala, Jashua Rajesh

PY - 2012/7/12

Y1 - 2012/7/12

N2 - Supervised learning is a popular approach to text classification among the research community as well as within software development industry. It enables intelligent systems to solve various text analysis problems such as document organization, spam detection and report scoring. However, the extremely difficult and time intensive process of creating a training corpus makes it inapplicable to many text classification problems. In this research, we explored the opportunities of addressing this pitfall by studying the ontological characteristics of document categories and grouping them under virtual super-categories to narrow down the search for a suitable category. Applying this method showed that classifier performance has greatly improved despite the relatively small size of the training corpus.

AB - Supervised learning is a popular approach to text classification among the research community as well as within software development industry. It enables intelligent systems to solve various text analysis problems such as document organization, spam detection and report scoring. However, the extremely difficult and time intensive process of creating a training corpus makes it inapplicable to many text classification problems. In this research, we explored the opportunities of addressing this pitfall by studying the ontological characteristics of document categories and grouping them under virtual super-categories to narrow down the search for a suitable category. Applying this method showed that classifier performance has greatly improved despite the relatively small size of the training corpus.

KW - Categorization

KW - Corporate Sustainability Report

KW - Feature Selection

KW - Global Reporting Initiative

KW - Machine Learning

KW - Supervised Learning

KW - Text Ontology

U2 - 10.1109/ICIMTR.2012.6236461

DO - 10.1109/ICIMTR.2012.6236461

M3 - Conference contribution

AN - SCOPUS:84864799173

SN - 9781467306553

SP - 574

EP - 579

BT - ICIMTR 2012 - 2012 International Conference on Innovation, Management and Technology Research

PB - IEEE

T2 - 2012 International Conference on Innovation, Management and Technology Research, ICIMTR 2012

Y2 - 21 May 2012 through 22 May 2012

ER -

Enhanced intelligent text categorization using concise keyword analysis

Abstract

Conference

Keywords

UN SDGs

Access to Document

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Cite this