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Text Classification by Augmenting Bag of Words (BOW) Representation with Co-occurrence Feature
Open accessAbstract
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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“Traditional methods for extracting emotional features use the bag-of-words (BoW) model [58,59].”
From A Survey of Deep Learning-Based Multimodal Emotion Recognition: Speech, Text, and Face (2023) · cites this paper as [58]
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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)
- ORCID
- Joseph, Shibily
- 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
- bronze
- doi.org
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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