An approach to automatic classification of text for information retrieval

Hong Cui; P. Bryan Heidorn; Hong Zhang

doi:10.1145/544220.544237

An approach to automatic classification of text for information retrieval

Hong Cui, P. Bryan Heidorn, Hong Zhang

Research output: Contribution to conference › Paper › peer-review

4 Scopus citations

Abstract

In this paper, we explore an approach to make better use of semi-structured documents in information retrieval in the domain of biology. Using machine learning techniques, we make those inherent structures explicit by XML markups. This marking up has great potentials in improving task performance in specimen identification and the usability of online flora and fauna.

Original language	English (US)
Pages	96-97
Number of pages	2
DOIs	https://doi.org/10.1145/544220.544237
State	Published - 2002
Externally published	Yes
Event	Proceedings of the Second ACM/IEEE-CS Joint Conference on Digital Libraries - Portland, OR, United States Duration: Jul 14 2002 → Jul 18 2002

Other

Other	Proceedings of the Second ACM/IEEE-CS Joint Conference on Digital Libraries
Country/Territory	United States
City	Portland, OR
Period	7/14/02 → 7/18/02

Keywords

Automatic classification
Flora of North America
Machine learning
XML

ASJC Scopus subject areas

Software
Information Systems
Computer Science Applications
Library and Information Sciences

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10.1145/544220.544237

Cite this

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title = "An approach to automatic classification of text for information retrieval",

abstract = "In this paper, we explore an approach to make better use of semi-structured documents in information retrieval in the domain of biology. Using machine learning techniques, we make those inherent structures explicit by XML markups. This marking up has great potentials in improving task performance in specimen identification and the usability of online flora and fauna.",

keywords = "Automatic classification, Flora of North America, Machine learning, XML",

author = "Hong Cui and Heidorn, {P. Bryan} and Hong Zhang",

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doi = "10.1145/544220.544237",

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AU - Heidorn, P. Bryan

AU - Zhang, Hong

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AB - In this paper, we explore an approach to make better use of semi-structured documents in information retrieval in the domain of biology. Using machine learning techniques, we make those inherent structures explicit by XML markups. This marking up has great potentials in improving task performance in specimen identification and the usability of online flora and fauna.

KW - Automatic classification

KW - Flora of North America

KW - Machine learning

KW - XML

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M3 - Paper

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T2 - Proceedings of the Second ACM/IEEE-CS Joint Conference on Digital Libraries

Y2 - 14 July 2002 through 18 July 2002

ER -

An approach to automatic classification of text for information retrieval

Abstract

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Keywords

ASJC Scopus subject areas

Access to Document

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