# Pipeline options - lemma is no-op for Chinese but currently needed because coref demands it (bad old requirements system)
annotators = segment, ssplit, pos, lemma, ner, parse, mention, coref

# segment
customAnnotatorClass.segment = edu.stanford.nlp.pipeline.ChineseSegmenterAnnotator

segment.model = edu/stanford/nlp/models/segmenter/chinese/ctb.gz
segment.sighanCorporaDict = edu/stanford/nlp/models/segmenter/chinese
segment.serDictionary = edu/stanford/nlp/models/segmenter/chinese/dict-chris6.ser.gz
segment.sighanPostProcessing = true

# sentence split
ssplit.boundaryTokenRegex = [.]|[!?]+|[。]|[！？]+

# pos
pos.model = edu/stanford/nlp/models/pos-tagger/chinese-distsim/chinese-distsim.tagger

# ner
ner.model = edu/stanford/nlp/models/ner/chinese.misc.distsim.crf.ser.gz
ner.applyNumericClassifiers = false
ner.useSUTime = false

# parse
parse.model = edu/stanford/nlp/models/lexparser/chineseFactored.ser.gz

# depparse
depparse.model    = edu/stanford/nlp/models/parser/nndep/PTB_CoNLL_params.txt.gz
depparse.language = chinese

# coref
coref.sieves = ChineseHeadMatch, ExactStringMatch, PreciseConstructs, StrictHeadMatch1, StrictHeadMatch2, StrictHeadMatch3, StrictHeadMatch4, PronounMatch
coref.input.type = raw
coref.postprocessing = true
coref.calculateFeatureImportance = false
coref.useConstituencyTree = true
coref.useSemantics = false
coref.md.type = RULE
coref.mode = hybrid
coref.path.word2vec =
coref.language = zh
coref.print.md.log = false
coref.defaultPronounAgreement = true
coref.zh.dict = edu/stanford/nlp/models/dcoref/zh-attributes.txt.gz


