Classifying biological articles using web resources
- 14 March 2004
- conference paper
- Published by Association for Computing Machinery (ACM)
- p. 111-115
- https://doi.org/10.1145/967900.967925
Abstract
Text classification systems on biomedical literature aim to select relevant articles to a specific issue from large corpora. Most systems with an acceptable accuracy are based on domain knowledge, which is very expensive and does not provide a general solution. This paper presents a novel approach for text classification on biomedical literature, involving the use of information extracted from related web resources. We validated this approach by implementing the proposed method and testing it on the KDD2002 Cup challenge: bio-text task. Results show that our approach can effectively improve efficiency on text classification systems for biomedical literature.Keywords
This publication has 10 references indexed in Scilit:
- Automatic scientific text classification using local patternsACM SIGKDD Explorations Newsletter, 2002
- Rule-based extraction of experimental evidence in the biomedical domainACM SIGKDD Explorations Newsletter, 2002
- Accomplishments and challenges in literature data mining for biologyBioinformatics, 2002
- Background and overview for KDD Cup 2002 task 1ACM SIGKDD Explorations Newsletter, 2002
- A machine learning approach for the curation of biomedical literatureACM SIGKDD Explorations Newsletter, 2002
- GenBankNucleic Acids Research, 2002
- Extracting information automatically from biological literatureComparative and Functional Genomics, 2001
- The use of the area under the ROC curve in the evaluation of machine learning algorithmsPattern Recognition, 1997
- Around the genomes: the Drosophila genome project.Genome Research, 1996
- Boolean Feature Discovery in Empirical LearningMachine Learning, 1990