annotators = tokenize, ssplit, pos, lemma, ner, depparse, coref, kbp

tokenize.language = en

# Some other annotators are also available for English and can be optionally loaded, e.g.:
# annotators = tokenize, ssplit, pos, lemma, truecase

# This is an example of the "full" pipeline, though there are even more annotators than this:
# annotators = tokenize,cleanxml,ssplit,pos,lemma,ner,parse,depparse,coref,natlog,openie,kbp,entitylink,sentiment,quote

# Options like the ones below are being set as defaults in code

# pos.model = edu/stanford/nlp/models/pos-tagger/english-left3words-distsim.tagger

# ner.model = edu/stanford/nlp/models/ner/english.all.3class.distsim.crf.ser.gz,\
#             edu/stanford/nlp/models/ner/english.muc.7class.distsim.crf.ser.gz,\
#             edu/stanford/nlp/models/ner/english.conll.4class.distsim.crf.ser.gz

# sutime.rules = edu/stanford/nlp/models/sutime/defs.sutime.txt,edu/stanford/nlp/models/sutime/english.sutime.txt,\
# edu/stanford/nlp/models/sutime/english.holidays.sutime.txt

# ner.fine.regexner.mapping =
# "ignorecase=true,validpospattern=(NN|JJ|ADD).*,edu/stanford/nlp/models/kbp/english/gazetteers/regexner_caseless.tab;\
#  edu/stanford/nlp/models/kbp/english/gazetteers/regexner_cased.tab"
# ner.fine.regexner.noDefaultOverwriteLabels = CITY

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

# depparse.model = edu/stanford/nlp/models/parser/nndep/english_UD.gz

# coref.algorithm = statistical
# coref.md.type = dependency
# coref.statistical.rankingModel = edu/stanford/nlp/models/coref/statistical/ranking_model.ser.gz