RELIABLE COGNITIVE DIMENSIONAL DOCUMENT RANKING BY WEIGHTED STANDARD CAUCHY DISTRIBUTION

Categorization of cognitively uniform and consistent documents such as University question papers are in demand by e-learners. Literature indicates that Standard Cauchy distribution and the derived values are extensively used for checking uniformity and consistency of documents. The paper attempts t...

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Main Authors: S Florence Vijila, K Nirmala
Format: Article
Language:English
Published: ICT Academy of Tamil Nadu 2017-04-01
Series:ICTACT Journal on Soft Computing
Subjects:
Online Access:http://ictactjournals.in/ArticleDetails.aspx?id=3013
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spelling doaj-0c4f9d13dcee4950988c84036f8912b42020-11-25T01:27:09ZengICT Academy of Tamil NaduICTACT Journal on Soft Computing0976-65612229-69562017-04-017314371442RELIABLE COGNITIVE DIMENSIONAL DOCUMENT RANKING BY WEIGHTED STANDARD CAUCHY DISTRIBUTIONS Florence Vijila0K Nirmala1Manonmaniam Sundaranar University, IndiaQuaid-e-Millath Government College for Women, IndiaCategorization of cognitively uniform and consistent documents such as University question papers are in demand by e-learners. Literature indicates that Standard Cauchy distribution and the derived values are extensively used for checking uniformity and consistency of documents. The paper attempts to apply this technique for categorizing question papers according to four selective cognitive dimensions. For this purpose cognitive dimensional keyword sets of these four categories (also termed as portrayal concepts) are assumed and an automatic procedure is developed to quantify these dimensions in question papers. The categorization is relatively accurate when checked with manual methods. Hence simple and well established term frequency / inverse document frequency ‘tf/ IDF’ technique is considered for automating the categorization process. After the documents categorization, standard Cauchy formula is applied to rank order the documents that have the least differences among Cauchy value, (according to Cauchy theorem) so as obtain consistent and uniform documents in an order or ranked. For the purpose of experiments and social survey, seven question papers (documents) have been designed with various consistencies. To validate this proposed technique social survey is administered on selective samples of e-learners of Tamil Nadu, India. Results are encouraging and conclusions drawn out of the experiments will be useful to researchers of concept mining and categorizing documents according to concepts. Findings have also contributed utility value to e-learning system designers.http://ictactjournals.in/ArticleDetails.aspx?id=3013Standard Cauchy distribution; Document Categorization; Concept extraction; Cognitive Dimensions; Term frequencies
collection DOAJ
language English
format Article
sources DOAJ
author S Florence Vijila
K Nirmala
spellingShingle S Florence Vijila
K Nirmala
RELIABLE COGNITIVE DIMENSIONAL DOCUMENT RANKING BY WEIGHTED STANDARD CAUCHY DISTRIBUTION
ICTACT Journal on Soft Computing
Standard Cauchy distribution; Document Categorization; Concept extraction; Cognitive Dimensions; Term frequencies
author_facet S Florence Vijila
K Nirmala
author_sort S Florence Vijila
title RELIABLE COGNITIVE DIMENSIONAL DOCUMENT RANKING BY WEIGHTED STANDARD CAUCHY DISTRIBUTION
title_short RELIABLE COGNITIVE DIMENSIONAL DOCUMENT RANKING BY WEIGHTED STANDARD CAUCHY DISTRIBUTION
title_full RELIABLE COGNITIVE DIMENSIONAL DOCUMENT RANKING BY WEIGHTED STANDARD CAUCHY DISTRIBUTION
title_fullStr RELIABLE COGNITIVE DIMENSIONAL DOCUMENT RANKING BY WEIGHTED STANDARD CAUCHY DISTRIBUTION
title_full_unstemmed RELIABLE COGNITIVE DIMENSIONAL DOCUMENT RANKING BY WEIGHTED STANDARD CAUCHY DISTRIBUTION
title_sort reliable cognitive dimensional document ranking by weighted standard cauchy distribution
publisher ICT Academy of Tamil Nadu
series ICTACT Journal on Soft Computing
issn 0976-6561
2229-6956
publishDate 2017-04-01
description Categorization of cognitively uniform and consistent documents such as University question papers are in demand by e-learners. Literature indicates that Standard Cauchy distribution and the derived values are extensively used for checking uniformity and consistency of documents. The paper attempts to apply this technique for categorizing question papers according to four selective cognitive dimensions. For this purpose cognitive dimensional keyword sets of these four categories (also termed as portrayal concepts) are assumed and an automatic procedure is developed to quantify these dimensions in question papers. The categorization is relatively accurate when checked with manual methods. Hence simple and well established term frequency / inverse document frequency ‘tf/ IDF’ technique is considered for automating the categorization process. After the documents categorization, standard Cauchy formula is applied to rank order the documents that have the least differences among Cauchy value, (according to Cauchy theorem) so as obtain consistent and uniform documents in an order or ranked. For the purpose of experiments and social survey, seven question papers (documents) have been designed with various consistencies. To validate this proposed technique social survey is administered on selective samples of e-learners of Tamil Nadu, India. Results are encouraging and conclusions drawn out of the experiments will be useful to researchers of concept mining and categorizing documents according to concepts. Findings have also contributed utility value to e-learning system designers.
topic Standard Cauchy distribution; Document Categorization; Concept extraction; Cognitive Dimensions; Term frequencies
url http://ictactjournals.in/ArticleDetails.aspx?id=3013
work_keys_str_mv AT sflorencevijila reliablecognitivedimensionaldocumentrankingbyweightedstandardcauchydistribution
AT knirmala reliablecognitivedimensionaldocumentrankingbyweightedstandardcauchydistribution
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