Merging words and concepts for medical articles retrieval

Authors:
Ivan Kitanovski;Katarina Trojacanec;Ivica Dimitrovski;Suzana Loshkovska
Affiliations:
University Ss.Cyril and Methodius, Skopje, Macedonia;University Ss.Cyril and Methodius, Skopje, Macedonia;University Ss.Cyril and Methodius, Skopje, Macedonia;University Ss.Cyril and Methodius, Skopje, Macedonia
Venue:
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
Year:
2013

Citing 8
Cited 0

Probabilistic models of information retrieval based on measuring the divergence from randomness

ACM Transactions on Information Systems (TOIS)
A study of smoothing methods for language models applied to information retrieval

ACM Transactions on Information Systems (TOIS)
Domain knowledge conceptual inter-media indexing: application to multilingual multimedia medical reports

Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Introduction to Information Retrieval

Introduction to Information Retrieval
MeSH Up

Bioinformatics
Score normalization in multimodal biometric systems

Pattern Recognition
University of glasgow at WebCLEF 2005: experiments in per-field normalisation and language specific stemming

CLEF'05 Proceedings of the 6th international conference on Cross-Language Evalution Forum: accessing Multilingual Information Repositories
Terrier information retrieval platform

ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper a strategy which uses a combination of word-space and concept-space approaches to solve the problem of medical articles retrieval is proposed. The word-space approach uses the Terrier IR search engine to index and retrieve the medical articles. The concept-space approach uses Metamap to map the text data into a set of UMLS concepts, which are later indexed and retrieved by the Terrier IR search engine. The results from the word-space and concept-space retrieval are fused using linear combination, which is among the simplest and the most frequently used methods. The results show that the fusion of word-space and concept-space improves the overall retrieval performance.