HDLTex: Hierarchical Deep Learning for Text Classification Article Swipe
Kamran Kowsari
,
Donald E. Brown
,
Mojtaba Heidarysafa
,
Kiana Jafari Meimandi
,
Matthew S. Gerber
,
Laura E. Barnes
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1109/icmla.2017.0-134
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1109/icmla.2017.0-134
The continually increasing number of documents produced each year\nnecessitates ever improving information processing methods for searching,\nretrieving, and organizing text. Central to these information processing\nmethods is document classification, which has become an important application\nfor supervised learning. Recently the performance of these traditional\nclassifiers has degraded as the number of documents has increased. This is\nbecause along with this growth in the number of documents has come an increase\nin the number of categories. This paper approaches this problem differently\nfrom current document classification methods that view the problem as\nmulti-class classification. Instead we perform hierarchical classification\nusing an approach we call Hierarchical Deep Learning for Text classification\n(HDLTex). HDLTex employs stacks of deep learning architectures to provide\nspecialized understanding at each level of the document hierarchy.\n
Related Topics
Concepts
Computer science
Artificial intelligence
Hierarchy
Document classification
Class (philosophy)
Deep learning
One-class classification
Machine learning
Supervised learning
Class hierarchy
Information retrieval
Artificial neural network
Classifier (UML)
Programming language
Economics
Object-oriented programming
Market economy
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/icmla.2017.0-134
- OA Status
- green
- Cited By
- 426
- References
- 62
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W2759474451Canonical identifier for this work in OpenAlex
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https://doi.org/10.1109/icmla.2017.0-134Digital Object Identifier
- Title
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HDLTex: Hierarchical Deep Learning for Text ClassificationWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
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2017Year of publication
- Publication date
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2017-12-01Full publication date if available
- Authors
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Kamran Kowsari, Donald E. Brown, Mojtaba Heidarysafa, Kiana Jafari Meimandi, Matthew S. Gerber, Laura E. BarnesList of authors in order
- Landing page
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https://doi.org/10.1109/icmla.2017.0-134Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/1709.08267Direct OA link when available
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Computer science, Artificial intelligence, Hierarchy, Document classification, Class (philosophy), Deep learning, One-class classification, Machine learning, Supervised learning, Class hierarchy, Information retrieval, Artificial neural network, Classifier (UML), Programming language, Economics, Object-oriented programming, Market economyTop concepts (fields/topics) attached by OpenAlex
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426Total citation count in OpenAlex
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2025: 44, 2024: 54, 2023: 72, 2022: 53, 2021: 74Per-year citation counts (last 5 years)
- References (count)
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62Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| publication_date | 2017-12-01 |
| publication_year | 2017 |
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