#location of the training file trainFile = testdata . tsv #location where you would like to save (serialize to) your #classifier; adding .gz at the end automatically gzips the file, #making it faster and smaller serializeTo = ner - model . ser . gz #structure of your training file; this tells the classifier #that the word is in column 0 and the correct answer is in #column 1 map = word = 0 , answer = 1 #these are the features we'd like to train with #some are discussed below, the rest can be #understood by looking at NERFeatureFactory useClassFeature = true useWord = true useNGrams = true #no ngrams will be included that do not contain either the #beginning or end of the word noMidNGrams = true useDisjunctive = true maxNGramLeng = 6 usePrev = true useNext = true useSequences = true usePrevSequences = true maxLeft = 1 #the next 4 deal with word shape features useTypeSeqs = true useTypeSeqs2 = true useTypeySequences = true wordShape = chris2useLC
