An experimental study on large-scale web categorization

Tie-Yan Liu; Yiming Yang; Hao Wan; Qian Zhou; Bin Gao; Hua-Jun Zeng; Zheng Chen; Wei-Ying Ma

doi:10.1145/1062745.1062891

An experimental study on large-scale web categorization

Tie-Yan Liu
, Yiming Yang
, Hao Wan
, Qian Zhou
, Bin Gao
, Hua-Jun Zeng
, Zheng Chen
, Wei-Ying Ma

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

23 Citations (Scopus)

Abstract

Taxonomies of the Web typically have hundreds of thousands of categories and skewed category distribution over documents. It is not clear whether existing text classification technologies can perform well on and scale up to such large-scale applications. To understand this, we conducted the evaluation of several representative methods (Support Vector Machines, k-Nearest Neighbor and Naive Bayes) with Yahoo! taxonomies. In particular, we evaluated the effectiveness/efficiency tradeoff in classifiers with hierarchical setting compared to conventional (flat) setting, and tested popular threshold tuning strategies for their scalability and accuracy in large-scale classification problems.
Copyright is held by the author/owner(s).

Original language	English
Title of host publication	14th International World Wide Web Conference, WWW2005
Pages	1106-1107
DOIs	https://doi.org/10.1145/1062745.1062891
Publication status	Published - 2005
Externally published	Yes
Event	14th International World Wide Web Conference, WWW2005 - Chiba, Japan Duration: 10 May 2005 → 14 May 2005

Publication series

Name	14th International World Wide Web Conference, WWW2005

Conference

Conference	14th International World Wide Web Conference, WWW2005
Place	Japan
City	Chiba
Period	10/05/05 → 14/05/05

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Research Keywords

Algorithm complexity
Parameter tuning strategies
Text categorization
Very large web taxonomies

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10.1145/1062745.1062891

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title = "An experimental study on large-scale web categorization",

abstract = "Taxonomies of the Web typically have hundreds of thousands of categories and skewed category distribution over documents. It is not clear whether existing text classification technologies can perform well on and scale up to such large-scale applications. To understand this, we conducted the evaluation of several representative methods (Support Vector Machines, k-Nearest Neighbor and Naive Bayes) with Yahoo! taxonomies. In particular, we evaluated the effectiveness/efficiency tradeoff in classifiers with hierarchical setting compared to conventional (flat) setting, and tested popular threshold tuning strategies for their scalability and accuracy in large-scale classification problems. Copyright is held by the author/owner(s).",

keywords = "Algorithm complexity, Parameter tuning strategies, Text categorization, Very large web taxonomies",

author = "Tie-Yan Liu and Yiming Yang and Hao Wan and Qian Zhou and Bin Gao and Hua-Jun Zeng and Zheng Chen and Wei-Ying Ma",

note = "Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].; 14th International World Wide Web Conference, WWW2005 ; Conference date: 10-05-2005 Through 14-05-2005",

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Liu, T-Y, Yang, Y, Wan, H, Zhou, Q, Gao, B, Zeng, H-J, Chen, Z & Ma, W-Y 2005, An experimental study on large-scale web categorization. in 14th International World Wide Web Conference, WWW2005. 14th International World Wide Web Conference, WWW2005, pp. 1106-1107, 14th International World Wide Web Conference, WWW2005, Chiba, Japan, 10/05/05. https://doi.org/10.1145/1062745.1062891

An experimental study on large-scale web categorization. / Liu, Tie-Yan; Yang, Yiming; Wan, Hao et al.
14th International World Wide Web Conference, WWW2005. 2005. p. 1106-1107 (14th International World Wide Web Conference, WWW2005).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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AU - Gao, Bin

AU - Zeng, Hua-Jun

AU - Chen, Zheng

AU - Ma, Wei-Ying

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

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AB - Taxonomies of the Web typically have hundreds of thousands of categories and skewed category distribution over documents. It is not clear whether existing text classification technologies can perform well on and scale up to such large-scale applications. To understand this, we conducted the evaluation of several representative methods (Support Vector Machines, k-Nearest Neighbor and Naive Bayes) with Yahoo! taxonomies. In particular, we evaluated the effectiveness/efficiency tradeoff in classifiers with hierarchical setting compared to conventional (flat) setting, and tested popular threshold tuning strategies for their scalability and accuracy in large-scale classification problems. Copyright is held by the author/owner(s).

KW - Algorithm complexity

KW - Parameter tuning strategies

KW - Text categorization

KW - Very large web taxonomies

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An experimental study on large-scale web categorization

Abstract

Publication series

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Bibliographical note

Research Keywords

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