英文:
Stanford Core NLP ner 4.0.0 Error: Could not find or load main class stanford-ner.jar;lib.*
问题
我正在尝试使用Stanford CoreNLP训练一个命名实体识别模型，但它找不到主类。我已经在我的CLASSPATH中添加了到jar文件的路径，但它仍然无法定位它们。有没有办法修复这个问题呢？
英文:
I am trying to train a NER model with Stanford CoreNLP however it cannot find the main class. I have included the paths to the jar files in my CLASSPATH yet it still cannot locate them. Any way to fix this please?
C:\Users\apaul\Desktop\CoreNLP\stanford-corenlp-4.0.0\stanford-ner-4.0.0>java -cp "C:\Users\apaul\Desktop\CoreNLP\stanford-ner-4.0.0*" -mx3g stanford-ner.jar;lib/* edu.stanford.nlp.ie.crf.CRFClassifier -loadClassifier classifiers\english.all.3class.distsim.crf.ser.gz -textFile file
Picked up _JAVA_OPTIONS: -Xmx256M
Error: Could not find or load main class stanford-ner.jar;lib.*
答案1
得分: 0
1.) 进入C:\Users\apaul\Desktop\CoreNLP\stanford-corenlp-4.0.0\stanford-ner-4.0.0目录。
2.) 创建一个训练 props 文件并将其放在该目录中。例如，为这个示例命名为 my-model.props。
# 训练数据的位置
trainFileList = /path/to/conll.3class.train
# 测试数据的位置
testFile = /path/to/all.3class.test
# 保存模型的位置
serializeTo = ner.model.ser.gz
type = crf
wordFunction = edu.stanford.nlp.process.AmericanizeFunction
useDistSim = false
# 确定数据文件格式
map = word=0,answer=1
saveFeatureIndexToDisk = true
useClassFeature=true
useWord=true
useNGrams=true
noMidNGrams=true
maxNGramLeng=6
usePrev=true
useNext=true
useLongSequences=true
useSequences=true
usePrevSequences=true
maxLeft=1
useTypeSeqs=true
useTypeSeqs2=true
useTypeySequences=true
useOccurrencePatterns=true
useLastRealWord=true
useNextRealWord=true
normalize=true
wordShape=chris2useLC
useDisjunctive=true
disjunctionWidth=5
readerAndWriter=edu.stanford.nlp.sequences.ColumnDocumentReaderAndWriter
useObservedSequencesOnly=true
useQN = true
QNsize = 25
# 提高运行速度
featureDiffThresh=0.05
3.) 在相同的目录中运行命令java -cp "*" -Xmx3g edu.stanford.nlp.ie.crf.CRFClassifier -prop my-model.props。
英文:
1.) cd into C:\Users\apaul\Desktop\CoreNLP\stanford-corenlp-4.0.0\stanford-ner-4.0.0
2.) Create a training props file and put it in that directory. For this example call it my-model.props.
# location of training data
trainFileList = /path/to/conll.3class.train
# location of test data
testFile = /path/to/all.3class.test
# where to store the saved model
serializeTo = ner.model.ser.gz
type = crf
wordFunction = edu.stanford.nlp.process.AmericanizeFunction
useDistSim = false
# establish the data file format
map = word=0,answer=1
saveFeatureIndexToDisk = true
useClassFeature=true
useWord=true
useNGrams=true
noMidNGrams=true
maxNGramLeng=6
usePrev=true
useNext=true
useLongSequences=true
useSequences=true
usePrevSequences=true
maxLeft=1
useTypeSeqs=true
useTypeSeqs2=true
useTypeySequences=true
useOccurrencePatterns=true
useLastRealWord=true
useNextRealWord=true
normalize=true
wordShape=chris2useLC
useDisjunctive=true
disjunctionWidth=5
readerAndWriter=edu.stanford.nlp.sequences.ColumnDocumentReaderAndWriter
useObservedSequencesOnly=true
useQN = true
QNsize = 25
# makes it go faster
featureDiffThresh=0.05
3.) run java -cp "*" -Xmx3g edu.stanford.nlp.ie.crf.CRFClassifier -prop my-model.props in the same directory.
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