map = word=0
loadBisequenceClassifierCh = cn.ser.gz
loadBisequenceClassifierEn = en.ser.gz

bisequenceClassifierPropCh = cn.prop
bisequenceClassifierPropEn = en.prop

bisequenceTestFileEn = en.test
bisequenceTestFileCh = cn.test
bisequenceTestAlignmentFile = test.align

bisequenceTestOutputEn = en.test.out
bisequenceTestOutputCh = cn.test.out
bisequenceAlignmentPriorPenaltyCh = autostat.penalty.3class
dualDecompNotBIO = true

readerAndWriter=edu.stanford.nlp.sequences.ColumnDocumentReaderAndWriter
initViterbi=true
annealingType=linear
numSamples=1000
bisequencePriorType=2
alignmentPruneThreshold=0.5
factorInAlignmentProb=true
useChromaticSampling=false
useSequentialScanSampling=true
maxAllowedChromaticSize=32
samplingSpeedUpThreshold=300
multiThreadGibbs = 8

## if uses global consistency model, uncomment the following lines
# useBilingualNERPrior=true
# matchNERIncentive=true
# entityMatrixCh = entity_matrix.cn
# entityMatrixEn = entity_matrix.en