Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Automated learning of decision rules for text categorization
ACM Transactions on Information Systems (TOIS)
Automatic Indexing: An Experimental Inquiry
Journal of the ACM (JACM)
A vector space model for automatic indexing
Communications of the ACM
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Classification of Web Documents Using a Graph Model
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Text classification using string kernels
The Journal of Machine Learning Research
Cyclic pattern kernels for predictive graph mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Optimal assignment kernels for attributed molecular graphs
ICML '05 Proceedings of the 22nd international conference on Machine learning
Shortest-Path Kernels on Graphs
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Graph-based text classification: learn from your neighbors
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Building semantic kernels for text classification using wikipedia
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Graph kernels based on tree patterns for molecules
Machine Learning
CGM: A biomedical text categorization approach using concept graph mining
BIBMW '09 Proceedings of the 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshop
Biomedical text categorization with concept graph representations using a controlled vocabulary
Proceedings of the 11th International Workshop on Data Mining in Bioinformatics
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Recently, graph representations of text have been showing improved performance over conventional bag-of-words representations in text categorization applications. In this paper, we present a graph-based representation for biomedical articles and use graph kernels to classify those articles into high-level categories. In our representation, common biomedical concepts and semantic relationships are identified with the help of an existing ontology and are used to build a rich graph structure that provides a consistent feature set and preserves additional semantic information that could improve a classifier's performance. We attempt to classify the graphs using both a set-based graph kernel that is capable of dealing with the disconnected nature of the graphs and a simple linear kernel. Finally, we report the results comparing the classification performance of the kernel classifiers to common text-based classifiers.