# Pipeline options - lemma is no-op for Chinese but currently needed because coref demands it (bad old requirements system)

#\u8BBE\u5B9A\u4E86\u7BA1\u9053\u4E2D\u5305\u62EC\u54EA\u4E9BAnnotators\uFF08\u4E00\u4E2AAnnotator\u5C31\u662F\u4F60\u9700\u8981\u7684\u6587\u672C\u5206\u6790\u5206\u6790\u5DE5\u5177\uFF0C \u4ED6\u7684\u7ED3\u679C\u5C31\u662F\u4E00\u4E2A\u6216\u591A\u4E2AAnnotation\uFF09
#segment:\u5206\u8BCD, ssplit:\u5206\u9694, pos: \u8BCD\u6027\u6807\u6CE8, lemma: has->have, ner:\u547D\u540D\u5B9E\u4F53\u8BC6\u522B, parse\uFF1A\u8BED\u6CD5\u5206\u6790
#annotators = segment, tokenize,ssplit, pos, lemma, ner, parse, sentiment, mention, coref
annotators = segment,tokenize, ssplit, pos,ner 


# segment \u5206\u8BCD
customAnnotatorClass.segment = edu.stanford.nlp.pipeline.ChineseSegmenterAnnotator
#segment.model = edu/stanford/nlp/models/segmenter/chinese/pku.gz
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
tokenize.language = zh
ssplit.isOneSentence = true
# sentence split
ssplit.boundaryTokenRegex = [.]|[!?]+|[\u3002]|[\uFF01\uFF1F]+

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

#ner \u6B64\u5904\u8BBE\u5B9A\u4E86ner\u4F7F\u7528\u7684\u8BED\u8A00\u3001\u6A21\u578B\uFF08crf\uFF09\uFF0C\u76EE\u524DSUTime\u53EA\u652F\u6301\u82F1\u6587\uFF0C\u4E0D\u652F\u6301\u4E2D\u6587\uFF0C\u6240\u4EE5\u8BBE\u7F6E\u4E3Afalse\u3002
ner.language = chinese
ner.model = edu/stanford/nlp/models/ner/chinese.misc.distsim.crf.ser.gz
ner.applyNumericClassifiers = true
ner.useSUTime = false

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

# 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
