C4.5: programs for machine learning
C4.5: programs for machine learning
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Extracting the names of genes and gene products with a hidden Markov model
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
RelEx---Relation extraction using dependency parse trees
Bioinformatics
Comparing information extraction pattern models
IEBeyondDoc '06 Proceedings of the Workshop on Information Extraction Beyond The Document
Comparative experiments on learning information extractors for proteins and their interactions
Artificial Intelligence in Medicine
The role of syntactic features in protein interaction extraction
Proceedings of the 2nd international workshop on Data and text mining in bioinformatics
| Hi-index | 0.00 |
Lexical variance in biomedical texts poses a challenge to automatic protein relation mining. We therefore propose a new approach that relies only on more general language structures such as parsing and dependency information for the construction of feature vectors that can be used by standard machine learning algorithms in deciding whether a sentence describes a protein interaction or not. As our approach is not dependent on the use of specific interaction keywords, it is applicable to heterogeneous corpora. Evaluation on benchmark datasets shows that our method is competitive with existing state-of-the-art algorithms for the extraction of protein interactions.