## tagger training invoked at Sun Sep 23 21:37:03 PST 2018 with arguments: model = english-bidirectional-distsim.tagger arch = bidirectional5words,allwordshapes(-1,1),distsim(/u/nlp/data/pos_tags_are_useless/egw4-reut.512.clusters,-1,1),distsimconjunction(/u/nlp/data/pos_tags_are_useless/egw4-reut.512.clusters,-1,1),rareExtractor(edu.stanford.nlp.tagger.maxent.ExtractorUCase),rareExtractor(edu.stanford.nlp.tagger.maxent.ExtractorCNumber),rareExtractor(edu.stanford.nlp.tagger.maxent.ExtractorDash),rareExtractor(edu.stanford.nlp.tagger.maxent.ExtractorLetterDigitDash),rareExtractor(edu.stanford.nlp.tagger.maxent.CompanyNameDetector),rareExtractor(edu.stanford.nlp.tagger.maxent.ExtractorAllCapitalized),rareExtractor(edu.stanford.nlp.tagger.maxent.ExtractorUpperDigitDash),rareExtractor(edu.stanford.nlp.tagger.maxent.ExtractorStartSentenceCap),rareExtractor(edu.stanford.nlp.tagger.maxent.ExtractorMidSentenceCapC),rareExtractor(edu.stanford.nlp.tagger.maxent.ExtractorMidSentenceCap),prefix(10),suffix(10),unicodeshapes(0),rareExtractor(edu.stanford.nlp.tagger.maxent.ExtractorNonAlphanumeric) wordFunction = edu.stanford.nlp.process.AmericanizeFunction trainFile = /u/nlp/data/pos-tagger/english/train-wsj-0-18;/u/nlp/data/pos-tagger/english/train-extra-english;/u/nlp/data/pos-tagger/english/train-tech-english;/u/nlp/data/pos-tagger/english/train-currency closedClassTags = closedClassTagThreshold = 40 curWordMinFeatureThresh = 2 debug = false debugPrefix = tagSeparator = _ encoding = UTF-8 iterations = 100 lang = english learnClosedClassTags = false minFeatureThresh = 2 openClassTags = rareWordMinFeatureThresh = 5 rareWordThresh = 5 search = owlqn sgml = false sigmaSquared = 0.5 regL1 = 0.75 tagInside = tokenize = true tokenizerFactory = tokenizerOptions = verbose = false verboseResults = true veryCommonWordThresh = 250 xmlInput = outputFile = outputFormat = slashTags outputFormatOptions = nthreads = 1