# The name of the corpus /see szte.csd.ContentShiftDetector.readCorpora()/
task = wiki
# The indicator selection method = [prob|condent|mi|mutalinfo|maxent]. For details see the Javadoc of the package szte.csd.indicatorsel
indicatorselector = prob
# The maximum length of indicator phrases (e.g. uni-, bi-, trigrams by phraselength=3)
phraselength = 2
# The number of co-training iterations
iterationNum = 3
# Whether to apply lemmatization
lemmatize = 1
# Whether the feature set of a local context contains syntactic parse-based features
syntaxFE = 0
# whether to log each prediction on each iteration (for significance tests)
logprediction = 0
# The path of the output serialized model
model = model.ser.gz

# If you use baseline_bow you switch on/off InfoGain feature selection (default is off)
baselineFeaturesel = 1
# If you don't want to learn a local CSD, instead you can define hand-crafted rule sets [sent|in-sent|bioscope]  for training and baselines as well
RBcsd = null
