我制作了一个CRF模型。我的数据集有24个类，此时我处于开始状态，因此我的训练数据只有1200个令牌/语料库。我训练了模型。在我的训练数据中，我使用了复数的令牌，如地址，照片，州，国家等。
现在在测试的时候，如果我以句子形式给这个模型提供多个令牌，那么它工作得很好但如果我以单数形式输入我的句子，如照片，状态等，那么它不会为其分配任何标签。
crf的这种行为看起来很奇怪。我已经探索了NER Feature Factory并使用了一些引理功能，但它也没有用。为模型构建共享cellFilter: 'number: 2', cellTooltip: 'Custom tooltip - maybe some help text'。
austen.prop通过阅读# location of the training file
trainFile = training_data_for_ner.txt
# location where you would like to save (serialize) your
# classifier; adding .gz at the end automatically gzips the file,
# making it smaller, and faster to load
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,pos=2,lemma=3
# This specifies the order of the CRF: order 1 means that features
# apply at most to a class pair of previous class and current class
# or current class and next class.
maxLeft=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
# word character ngrams will be included up to length 6 as prefixes
# and suffixes only
useNGrams=true
noMidNGrams=true
maxNGramLeng=6
usePrev=true
useNext=true
useDisjunctive=true
useSequences=true
usePrevSequences=true
# the last 4 properties deal with word shape features
useTypeSeqs=true
useTypeSeqs2=true
useTypeySequences=true
wordShape=chris2useLC
# newly added features.
useLemmas=true
usePrevNextLemmas=true
useLemmaAsWord=true
useTags=true
添加了最后四个功能。如果有人能帮助我解决这个问题，那么我将感激你。
答案 0 :(得分：0)
你应该使用带有标记的代币进行重新训练。例如，请参阅https://github.com/stanfordnlp/CoreNLP/blob/master/src/edu/stanford/nlp/process/Stemmer.java（main方法）。