Conceptualization Effects on MEDLINE Documents Classification Using Rocchio Method

Authors:
Shereen Albitar;Sebastien Fournier;Bernard Espinasse
Affiliations:
-;-;-
Venue:
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Year:
2012

Citing 8
Cited 0

OHSUMED: an interactive retrieval evaluation and new large test collection for research

SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Approximate statistical tests for comparing supervised classification learning algorithms

Neural Computation
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features

ECML '98 Proceedings of the 10th European Conference on Machine Learning
Building semantic kernels for text classification using wikipedia

Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Ontology-based MEDLINE document classification

BIRD'07 Proceedings of the 1st international conference on Bioinformatics research and development
A comparative study of ontology based term similarity measures on PubMed document clustering

DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Boosting for text classification with semantic features

WebKDD'04 Proceedings of the 6th international conference on Knowledge Discovery on the Web: advances in Web Mining and Web Usage Analysis
Ontology-guided feature engineering for clinical text classification

Journal of Biomedical Informatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

The aim of this paper is to propose a supervised text classification method for the biomedical domain using semantic resources. We choose the traditional text classification method, Rocchio, for its scalability and extendibility with semantic knowledge. This paper proposes to integrate semantic aspects into Rocchio through a conceptualization task. This conceptualization is realized by mapping terms that are extracted from text to their corresponding concepts in the UMLS® Metathesaurus® in order to take meaning into consideration during text classification. The proposed classifier is tested on the Ohsumed text corpus, which is composed of abstracts of biomedical articles retrieved from the MEDLINE® database. The effects of Conceptualization on Rocchio's performance are discussed according to different standard similarity measures and to a variety of conceptualization strategies.