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A semantic weighting method for document classification based on Markov logic networks

Authors:

Pankoo KimAuthors Info & Claims

RACS '14: Proceedings of the 2014 Conference on Research in Adaptive and Convergent Systems

Pages 29 - 33

https://doi.org/10.1145/2663761.2664212

Published: 05 October 2014 Publication History

Abstract

This paper proposes a semantic weighting method to classify textural documents. Human lives in the world where web documents have a great potential and the amount of valuable information has been consistently growing over the year. There is a problem that finding relevant web documents corresponding to what users want is more difficult due to the huge amount of web size. For this reason, there have been many researchers overcome this problem. The most important thing is document classification. All documents are composed of numerous words. Many classification methods have been extracted keywords from documents and then analyzed keywords pattern or frequency. In this paper, we propose Category Term Weight (CTW) using keywords from documents in order to enhance performance in document classification. CTW combines keywords frequency with semantic information. The frequency and semantic information have a great potential to find similarities between documents. That is why we calculates CTW from collection of training documents. After this step, CTW from unknown document and CTW in previous Category Term Database will be applied by designed Markov Logic Networks Model. Our designed MLNs Model and existing Naive-bayse Model will be compared by applied CTW. The experimental results shows the improvement of precision compare with the existing model.

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Cited By

Ruan DPing XGao K(2016)Markov logic networks based emotion classification for Chinese microblogsInternational Journal of Intelligent Information and Database Systems10.1504/IJIIDS.2016.0754329:2(197-211)Online publication date: 1-Mar-2016
https://dl.acm.org/doi/10.1504/IJIIDS.2016.075432

Index Terms

A semantic weighting method for document classification based on Markov logic networks
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
    1. Learning settings

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Published In

cover image ACM Conferences

RACS '14: Proceedings of the 2014 Conference on Research in Adaptive and Convergent Systems

October 2014

386 pages

ISBN:9781450330602

DOI:10.1145/2663761

Conference Chairs:
Chao Lu
Towson University
,
Esmaeil Nadimi
University of Southern Denmark, denmark
,
Program Chairs:
Sung-Ryul Kim
Konkuk University, Korea
,
Wei Wang
San Diego State University

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Winway Systems: Winway Systems, Inc.
SIGAPP: ACM Special Interest Group on Applied Computing
ACCT: Association of Convergent Computing Technology
TU: Towson University

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 October 2014

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RACS '14

Sponsor:

Winway Systems
SIGAPP
ACCT
TU

RACS '14: International Conference on Research in Adaptive and Convergent Systems

October 5 - 8, 2014

Maryland, Towson

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RACS '14 Paper Acceptance Rate 59 of 251 submissions, 24%;

Overall Acceptance Rate 393 of 1,581 submissions, 25%

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Cited By

Ruan DPing XGao K(2016)Markov logic networks based emotion classification for Chinese microblogsInternational Journal of Intelligent Information and Database Systems10.1504/IJIIDS.2016.0754329:2(197-211)Online publication date: 1-Mar-2016
https://dl.acm.org/doi/10.1504/IJIIDS.2016.075432

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