The nature of statistical learning theory
The nature of statistical learning theory
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Classification from full text: a comparison of canonical sections of scientific papers
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Feature generation for text categorization using world knowledge
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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The field of molecular biology is growing at an astounding rate and research findings are being deposited into public databases, such as Swiss-Prot. Many of the over 200,000 protein entries in Swiss-Prot 49.1 lack annotations such as subcellular localization or function, but the vast majority have references to journal abstracts describing related research. These abstracts represent a huge amount of information that could be used to generate annotations for proteins automatically. Training classifiers to perform text categorization on abstracts is one way to accomplish this task. We present a method for improving text classification for biological journal abstracts by generating additional text features using the knowledge represented in a biological concept hierarchy (the Gene Ontology). The structure of the ontology, as well as the synonyms recorded in it, are leveraged by our simple technique to significantly improve the F-measure of subcellular localization text classifiers by as much as 0.078 and we achieve F-measures as high as 0.935.