Extracting Protein-Protein Interaction Information from Biomedical Text with SVM
IEICE - Transactions on Information and Systems
A shortest path dependency kernel for relation extraction
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
ConText: an algorithm for identifying contextual features from clinical text
BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
A Machine Learning Approach for Identifying Disease-Treatment Relations in Short Texts
IEEE Transactions on Knowledge and Data Engineering
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This paper describes research methodologies and experimental settings for the task of relation identification and classification between pairs of medical entities, using clinical data. The models that we use represent a combination of lexical and syntactic features, medical semantic information, terms extracted from a vector-space model created using a random projection algorithm, and additional contextual information extracted at sentence-level. The best results are obtained using an SVM classification algorithm with a combination of the above mentioned features, plus a set of additional features that capture the distributional semantic correlation between the concepts and each relation of interest.