-
Modules:
- corpus.py: build corpus data and all mentions pairs
- feature.py: mention class and mention pair class, feature engineering here
- get_files.py: generate scorer readable gold and response files
- run.py: the main module to run
- best performance: MUC - 66.14%
-
Usage:
- in command line interface, type in: python run.py -h to see help message
- to only quickly check the TEST evaluation result (we have the trained model and test result given):
- perl scorers/scorer.pl all gold.txt response.txt "none"
- to run both TRAIN and TEST procedures, follow the steps below (may take around 20 mins to train):
- python run.py train : to train the model
- python run.py test : to test the trained model on test data
- perl scorers/scorer.pl all gold.txt response.txt "none" : to evaluate the response with all metric measurements
- to test on DEV data with already trained model:
- python run.py dev
- perl scorers/scorer.pl all gold.txt response.txt "none"
-
For developers:
- to improve the performance, only need to tune the feature.py and model in line 47 in run.py.
-
Notes: If "too many repeated mentions in the response" happens during evaluation with scorer.pl, please try to evaluate that failed measure metric separately. This seems to be a bug from the provided scorer.pl.
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