Machine Learning
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
The relationship between Precision-Recall and ROC curves
ICML '06 Proceedings of the 23rd international conference on Machine learning
Dependency tree kernels for relation extraction
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
RelEx---Relation extraction using dependency parse trees
Bioinformatics
Kernel approaches for genic interaction extraction
Bioinformatics
A graph kernel for protein-protein interaction extraction
BioNLP '08 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Comparative experiments on learning information extractors for proteins and their interactions
Artificial Intelligence in Medicine
DEEPER: a full parsing based approach to protein relation extraction
EvoBIO'08 Proceedings of the 6th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Extracting Protein Interactions from Text with the Unified AkaneRE Event Extraction System
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Most approaches for protein interaction mining from biomedical texts use both lexical and syntactic features. However, the individual impact of these two kinds of features on the effectiveness of the mining process has not yet been thoroughly studied. In this paper, we perform such a study on a recently published state of the art support vector machine approach that uses both lexical and syntactic features. To this end, we strip this approach down to an algorithm that uses only a subset of the initial syntactic features. Next, we compare the original and the stripped-down method by evaluating them on 5 benchmark datasets as well as by performing 5 additional cross-dataset experiments. Although the original method exploits a very rich feature set including words, parts-of-speech and grammatical relations, it is not significantly better than the stripped-down version; in fact, the former does not even consistently outperform the latter.