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Table 3 Runs submitted for ACT

From: Simple and efficient machine learning frameworks for identifying protein-protein interaction relevant articles and experimental methods used to study the interactions

Run number Label Classifier Algorithm Type of features used Number of features Training data
1 NBM-12-1k-td NBM Unigrams and Bigrams 1000 Training+Development
2 NBM-12-400-td NBM Unigrams and Bigrams 400 Training+Development
3 NBM-12-1k-d NBM Unigrams and Bigrams 1000 Development
4 SVM-12-400-d SVM Unigrams and Bigrams 400 Development
5 SVM-12-400-td SVM Unigrams and Bigrams 400 Training+Development
6 NBM-1-1k-td NBM Unigrams 1000 Training+Development
7 NBM-1-400-td NBM Unigrams 400 Training+Development
8 NBM-1-1k-d NBM Unigrams 1000 Development
9 SVM-1-400-d SVM Unigrams 400 Development
10 SVM-1-400-td SVM Unigrams 400 Training+Development
  1. For the BioCreative III challenge, each participating team was allowed to submit 10 runs for ACT. Five runs could be submitted offline and the other five runs could be submitted online, using XML-RPC. Runs 1-5 were submitted offline, while runs 6-10 were submitted online. For all runs, we used mutual information feature selection algorithm, as it gave better performance than chi-square score. We submitted 10 runs, listed here.