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properties

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default.properties

-# StanfordCoreNLP.properties
 annotators = tokenize, ssplit, pos, lemma, ner,  dcoref
-nnotators=tokenize,ssplit,pos,lemma,ner,parse,coref
-pos.model=models/left3words-wsj-0-18.tagger
-ner.model.3class=models/ner-en-3class.crf.gz
-ner.model.7class=models/muc.7class.crf.gz
-ner.model.distsim=models/conll.distsim.crf.ser.gz
-#nfl.gazetteer = models/NFLgazetteer.txt
-#nfl.relation.model = models/nfl_relation_model.ser
-parser.model=models/englishPCFG.ser.gz
-coref.model=models/coref/corefClassifierAll.March2009.ser.gz
-coref.name.dir=models/coref
-wordnet.dir=models/wordnet-3.0-prolog
+
+# A true-casing annotator is also available (see below)
+#annotators = tokenize, ssplit, pos, lemma, truecase
+
+# A simple regex NER annotator is also available
+# annotators = tokenize, ssplit, regexner
+
+#Use these as EOS punctuation and discard them from the actual sentence content
+#These are HTML tags that get expanded internally to correct syntax, e.g., from "p" to "<p>", "</p>" etc.
+#Will have no effect if the "cleanxml" annotator is used
+#ssplit.htmlBoundariesToDiscard = p,text
+
+#
+# None of these paths are necessary anymore: we load all models from the JAR file
+#
+
+#pos.model = /u/nlp/data/pos-tagger/wsj3t0-18-left3words/left3words-distsim-wsj-0-18.tagger
+## slightly better model but much slower:
+##pos.model = /u/nlp/data/pos-tagger/wsj3t0-18-bidirectional/bidirectional-distsim-wsj-0-18.tagger
+
+#ner.model.3class = /u/nlp/data/ner/goodClassifiers/all.3class.distsim.crf.ser.gz
+#ner.model.7class = /u/nlp/data/ner/goodClassifiers/muc.distsim.crf.ser.gz
+#ner.model.MISCclass = /u/nlp/data/ner/goodClassifiers/conll.distsim.crf.ser.gz
+
+#regexner.mapping = /u/nlp/data/TAC-KBP2010/sentence_extraction/type_map_clean
+#regexner.ignorecase = false
+
+#nfl.gazetteer = /scr/nlp/data/machine-reading/Machine_Reading_P1_Reading_Task_V2.0/data/SportsDomain/NFLScoring_UseCase/NFLgazetteer.txt
+#nfl.relation.model =  /scr/nlp/data/ldc/LDC2009E112/Machine_Reading_P1_NFL_Scoring_Training_Data_V1.2/models/nfl_relation_model.ser
+#nfl.entity.model =  /scr/nlp/data/ldc/LDC2009E112/Machine_Reading_P1_NFL_Scoring_Training_Data_V1.2/models/nfl_entity_model.ser
+#printable.relation.beam = 20
+
+#parser.model = /u/nlp/data/lexparser/englishPCFG.ser.gz
+
+#srl.verb.args=/u/kristina/srl/verbs.core_args
+#srl.model.cls=/u/nlp/data/srl/trainedModels/englishPCFG/cls/train.ann
+#srl.model.id=/u/nlp/data/srl/trainedModels/englishPCFG/id/train.ann
+
+#coref.model=/u/nlp/rte/resources/anno/coref/corefClassifierAll.March2009.ser.gz
+#coref.name.dir=/u/nlp/data/coref/
+#wordnet.dir=/u/nlp/data/wordnet/wordnet-3.0-prolog
+
+#dcoref.demonym = /scr/heeyoung/demonyms.txt
+#dcoref.animate = /scr/nlp/data/DekangLin-Animacy-Gender/Animacy/animate.unigrams.txt
+#dcoref.inanimate = /scr/nlp/data/DekangLin-Animacy-Gender/Animacy/inanimate.unigrams.txt
+#dcoref.male = /scr/nlp/data/Bergsma-Gender/male.unigrams.txt
+#dcoref.neutral = /scr/nlp/data/Bergsma-Gender/neutral.unigrams.txt
+#dcoref.female = /scr/nlp/data/Bergsma-Gender/female.unigrams.txt
+#dcoref.plural = /scr/nlp/data/Bergsma-Gender/plural.unigrams.txt
+#dcoref.singular = /scr/nlp/data/Bergsma-Gender/singular.unigrams.txt
+
+
+# This is the regular expression that describes which xml tags to keep
+# the text from.  In order to on off the xml removal, add cleanxml
+# to the list of annotators above after "tokenize".
+#clean.xmltags = .*
+# A set of tags which will force the end of a sentence.  HTML example:
+# you would not want to end on <i>, but you would want to end on <p>.
+# Once again, a regular expression.  
+# (Blank means there are no sentence enders.)
+#clean.sentenceendingtags =
+# Whether or not to allow malformed xml
+# StanfordCoreNLP.properties
+#wordnet.dir=models/wordnet-3.0-prolog