Cited by 95Text and Document Classification TechnologiesTopic ModelingNatural Language Processing Techniques

The DOI matches its Crossref record; 17 of the 19 references it cites were found in a registry or an index.

Text Classification by Augmenting Bag of Words (BOW) Representation with Co-occurrence Feature

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Department of Computer Science and Engineering, Government Engineering College, Thrissur, India, George K, Soumya, Joseph, Shibily·2014·IOSR Journal of Computer Engineering

Abstract

Text classification is the task of assigning predefined categories to free-text documents based on their content.Traditional approaches used unigram based models for text classification.Unigram based models such as Bag Of Words(BOW) models are not considering co-occurrence of set of words in a document level.This paper proposes a way to find co-occurrence feature from anchor text of wikipedia pages, proposes a way to incorporate co-occurrence feature to BOW model.Finally the method is analyzed to know how it performs in task of text classification.

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Metadata

Authors
Department of Computer Science and Engineering, G. E. C., George K, S., & Joseph, S.
Affiliations
Government Engineering College Thrissur (IN)
Venue
IOSR Journal of Computer Engineering
Publisher
IOSR Journals
Published
2014
Volume
16
Issue
1
Pages
34-38
ISSN
  • 2278-0661 (ISSN-L)
  • 2278-8727
OpenAlex
W2334175399
Cited by
94
References
19
Citations by year
2026: 1 · 2025: 7 · 2024: 10 · 2023: 14 · 2022: 10 · 2021: 23 · 2020: 12 · 2019: 8 · 2018: 5 · 2017: 3 · 2015: 1
FWCI
1.33
Type
journal article
Language
en
Open access
Locations
IOSR Journal of Computer Engineering (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
Keywords
text classification, bag-of-words, co-occurrence features, word co-occurrence, feature augmentation

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