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
|
- 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.