# This is better than Jenny's either with or without distsim turned on
# And using iob2 is better for optimal CoNLL performance.
# Features titled "chris2009"

trainFile = /u/nlp/data/ner/column_data/conll.4class.train
# testFile = /u/nlp/data/ner/column_data/conll.4class.testa
serializeTo = english.conll.4class.caseless.distsim.crf.ser.gz

wordFunction = edu.stanford.nlp.process.LowercaseAndAmericanizeFunction
useKnownLCWords = false

useDistSim = true
distSimLexicon = /u/nlp/data/pos_tags_are_useless/egw4-reut.512.clusters
# right options for egw4-reut.512 (though effect of having or not is small)
numberEquivalenceDistSim = true
unknownWordDistSimClass = 0

map = word=0,answer=1

saveFeatureIndexToDisk = true

useTitle = true
useClassFeature=true
useWord=true
# useWordPairs=true
useNGrams=true
noMidNGrams=true
# maxNGramLeng=6 # Having them all helps, which is the default
usePrev=true
useNext=true
# useTags=true
# useWordTag=true
useLongSequences=true
useSequences=true
usePrevSequences=true
maxLeft=1
useTypeSeqs=true
useTypeSeqs2=true
useTypeySequences=true
useOccurrencePatterns=true
useLastRealWord=true
useNextRealWord=true
#useReverse=false
normalize=true
# normalizeTimex=true
# dan2 better than chris2 on CoNLL data...
wordShape=dan2useLC
useDisjunctive=true
# disjunctionWidth 4 is better than 5 on CoNLL data
disjunctionWidth=4
#useDisjunctiveShapeInteraction=true

type=crf

readerAndWriter=edu.stanford.nlp.sequences.ColumnDocumentReaderAndWriter

useObservedSequencesOnly=true

sigma = 20
useQN = true
QNsize = 25

# makes it go faster
featureDiffThresh=0.05
