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Review on Pattern based Document Modelling Techniques

by Jimsy Johnson, Smitha C.S.
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 132 - Number 15
Year of Publication: 2015
Authors: Jimsy Johnson, Smitha C.S.
10.5120/ijca2015907653

Jimsy Johnson, Smitha C.S. . Review on Pattern based Document Modelling Techniques. International Journal of Computer Applications. 132, 15 ( December 2015), 1-5. DOI=10.5120/ijca2015907653

@article{ 10.5120/ijca2015907653,
author = { Jimsy Johnson, Smitha C.S. },
title = { Review on Pattern based Document Modelling Techniques },
journal = { International Journal of Computer Applications },
issue_date = { December 2015 },
volume = { 132 },
number = { 15 },
month = { December },
year = { 2015 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume132/number15/23667-2015907653/ },
doi = { 10.5120/ijca2015907653 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:29:27.857774+05:30
%A Jimsy Johnson
%A Smitha C.S.
%T Review on Pattern based Document Modelling Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 132
%N 15
%P 1-5
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Topic Modelling has been widely used in the fields of machine learning, text mining etc. It was proposed to generate statistical models to classify multiple topics in a collection of document, and each topic is represented by distribution of words. But many variants of topic models have been proposed and most of them are based on the concept of bag-of-words and it ignores the association of words for representing topics. Nowadays patterns are used for representing topics, since they have more discriminative power than words for representing multiple topics in a document. A detailed survey of some of the most important methods for topic modelling is presented. A brief comparison among the key techniques is also presented to complete the survey.

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Index Terms

Computer Science
Information Sciences

Keywords

Information Retrieval Information Filtering Topic models

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