An approach to automatic classification of text for information retrieval

Authors:
Hong Cui;P. Bryan Heidorn;Hong Zhang
Affiliations:
University of Illinois at Urbana-Champaign;University of Illinois at Urbana-Champaign;University of Illinois at Urbana-Champaign
Venue:
Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries
Year:
2002

Citing 3
Cited 4

Learning Information Extraction Rules for Semi-Structured and Free Text

Machine Learning - Special issue on natural language learning
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features

ECML '98 Proceedings of the 10th European Conference on Machine Learning

The reusability of induced knowledge for the automatic semantic markup of taxonomic descriptions

Journal of the American Society for Information Science and Technology
A Novel Retrieval Refinement and Interaction Pattern by Exploring Result Correlations for Image Retrieval

Adaptive Multimedial Retrieval: Retrieval, User, and Semantics
A hidden Markov model-based text classification of medical documents

Journal of Information Science
Semantic annotation of biosystematics literature without training examples

Journal of the American Society for Information Science and Technology

Quantified Score

Hi-index	0.00

Visualization

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.