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Hierarchical classification of Web content

Authors:

Hao ChenAuthors Info & Claims

SIGIR '00: Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval

Pages 256 - 263

https://doi.org/10.1145/345508.345593

Published: 01 July 2000 Publication History

Abstract

This paper explores the use of hierarchical structure for classifying a large, heterogeneous collection of web content. The hierarchical structure is initially used to train different second-level classifiers. In the hierarchical case, a model is learned to distinguish a second-level category from other categories within the same top level. In the flat non-hierarchical case, a model distinguishes a second-level category from all other second-level categories. Scoring rules can further take advantage of the hierarchy by considering only second-level categories that exceed a threshold at the top level.

We use support vector machine (SVM) classifiers, which have been shown to be efficient and effective for classification, but not previously explored in the context of hierarchical classification. We found small advantages in accuracy for hierarchical models over flat models. For the hierarchical approach, we found the same accuracy using a sequential Boolean decision rule and a multiplicative decision rule. Since the sequential approach is much more efficient, requiring only 14%-16% of the comparisons used in the other approaches, we find it to be a good choice for classifying text into large hierarchical structures.

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http://www.looksmart.com]]

Cited By

Kumar AToshniwal D(2024)Local Hierarchy-Aware Text-Label Association for Hierarchical Text Classification2024 IEEE 11th International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA61799.2024.10722840(1-10)Online publication date: 6-Oct-2024
https://doi.org/10.1109/DSAA61799.2024.10722840
Schmidt FHammerfald KJahren HPayberah AVlassov V(2024)Single-pass Hierarchical Text Classification with Large Language Models2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825412(5412-5421)Online publication date: 15-Dec-2024
https://doi.org/10.1109/BigData62323.2024.10825412
Sergin NHuang JChang TYan H(2024)Image-based novel fault detection with deep learning classifiers using hierarchical labelsIISE Transactions10.1080/24725854.2024.232606856:10(1112-1130)Online publication date: 2-May-2024
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Index Terms

Hierarchical classification of Web content
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees
2. Information systems
  1. Information retrieval
  2. Information storage systems

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Published In

cover image ACM Conferences

SIGIR '00: Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval

July 2000

396 pages

ISBN:1581132263

DOI:10.1145/345508

Chairmen:
Emmanuel Yannakoudakis
Athens Univ. of Economics and Business, Greece
,
Nicholas J. Belkin
Rutgers Univ.
,
Mun-Kew Leong
Kent Ridge Digital Labs
,
Peter Ingwersen
Royal School of Library and Information Science

Copyright © 2000 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

Greek Com Soc: Greek Computer Society
SIGIR: ACM Special Interest Group on Information Retrieval
Athens U of Econ & Business: Athens University of Economics and Business

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 July 2000

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SIGIR00

Sponsor:

Greek Com Soc
SIGIR
Athens U of Econ & Business

SIGIR00: 23rd ACM International SIGIR Conference on Research and Development in Information Retrieval

July 24 - 28, 2000

Athens, Greece

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Cited By

Kumar AToshniwal D(2024)Local Hierarchy-Aware Text-Label Association for Hierarchical Text Classification2024 IEEE 11th International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA61799.2024.10722840(1-10)Online publication date: 6-Oct-2024
https://doi.org/10.1109/DSAA61799.2024.10722840
Schmidt FHammerfald KJahren HPayberah AVlassov V(2024)Single-pass Hierarchical Text Classification with Large Language Models2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825412(5412-5421)Online publication date: 15-Dec-2024
https://doi.org/10.1109/BigData62323.2024.10825412
Sergin NHuang JChang TYan H(2024)Image-based novel fault detection with deep learning classifiers using hierarchical labelsIISE Transactions10.1080/24725854.2024.232606856:10(1112-1130)Online publication date: 2-May-2024
https://doi.org/10.1080/24725854.2024.2326068
du Toit JDunaiski M(2024)Prompt tuning discriminative language models for hierarchical text classificationNatural Language Processing10.1017/nlp.2024.51(1-18)Online publication date: 10-Oct-2024
https://doi.org/10.1017/nlp.2024.51
Chu YQian Y(2024)Incremental classification of remote sensing images using feature pyramid and class hierarchy enhanced by label relationship graphsApplied Intelligence10.1007/s10489-024-06216-055:3Online publication date: 28-Dec-2024
https://doi.org/10.1007/s10489-024-06216-0
Kumar AToshinwal D(2024)HLC: hierarchically-aware label correlation for hierarchical text classificationApplied Intelligence10.1007/s10489-023-05257-154:2(1602-1618)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s10489-023-05257-1
Zuo JHacid H(2024)Re-thinking Human Activity Recognition with Hierarchy-Aware Label Relationship ModelingAdvances in Knowledge Discovery and Data Mining10.1007/978-981-97-2262-4_1(3-14)Online publication date: 25-Apr-2024
https://doi.org/10.1007/978-981-97-2262-4_1
Jo TJo T(2024)Text Categorization: Conceptual ViewText Mining10.1007/978-3-031-75976-5_5(81-102)Online publication date: 8-Oct-2024
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Kumar AToshniwal D(2024)Modeling Text-Label Alignment for Hierarchical Text ClassificationMachine Learning and Knowledge Discovery in Databases. Research Track10.1007/978-3-031-70365-2_10(163-179)Online publication date: 8-Sep-2024
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Kumar AToshniwal D(2023)Label Hierarchy Alignment for Improved Hierarchical Text Classification2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386495(1174-1179)Online publication date: 15-Dec-2023
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