我正在用自己的训练数据重新训练斯坦福大学NER模型来提取组织，但是，无论我使用4GB RAM的机器还是8 GB RAM的机器，我都会得到相同的Java堆空间错误。
有谁能告诉我们，在没有这些内存问题的情况下，我们可以重新训练模型的机器的一般配置是什么？
我使用了以下命令：

java -mx4g -cp stanford-ner.jar edu.stanford.nlp.ie.crf.CRFClassifier -prop newdata_retrain.prop

我正在处理训练数据（多个文件-每个文件大约有15000行，格式如下）-每行一个单词及其类别
她是O在O微软组织工作
我们还能做些什么来让这些模型可靠地运行吗？我确实尝试过减少训练数据中的类别数量。但这会影响提取的准确性。例如，一些位置或其他实体被分类为组织名称。我们能在不影响准确性的情况下减少特定类别的数量吗？
我使用的一个数据是Alan Ritter的twitter nlp数据：https://github.com/aritter/twitter_nlp/tree/master/data/annotated/ner.txt
属性文件如下所示：

#location of the training file
trainFile = ner.txt
#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-twitter.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
saveFeatureIndexToDisk = true

我得到的错误：stacktrace是这样的：

CRFClassifier invoked on Mon Dec 01 02:55:22 UTC 2014 with arguments:
   -prop twitter_retrain.prop
usePrevSequences=true
useClassFeature=true
useTypeSeqs2=true
useSequences=true
wordShape=chris2useLC
saveFeatureIndexToDisk=true
useTypeySequences=true
useDisjunctive=true
noMidNGrams=true
serializeTo=ner-model-twitter.ser.gz
maxNGramLeng=6
useNGrams=true
usePrev=true
useNext=true
maxLeft=1
trainFile=ner.txt
map=word=0,answer=1
useWord=true
useTypeSeqs=true
[1000][2000]numFeatures = 215032
setting nodeFeatureIndicesMap, size=149877
setting edgeFeatureIndicesMap, size=65155
Time to convert docs to feature indices: 4.4 seconds
numClasses: 21 [0=O,1=B-facility,2=I-facility,3=B-other,4=I-other,5=B-company,6=B-person,7=B-tvshow,8=B-product,9=B-sportsteam,10=I-person,11=B-geo-loc,12=B-movie,13=I-movie,14=I-tvshow,15=I-company,16=B-musicartist,17=I-musicartist,18=I-geo-loc,19=I-product,20=I-sportsteam]
numDocuments: 2394
numDatums: 46469
numFeatures: 215032
Time to convert docs to data/labels: 2.5 seconds
Writing feature index to temporary file.
numWeights: 31880772
QNMinimizer called on double function of 31880772 variables, using M = 25.
Exception in thread "main" java.lang.OutOfMemoryError: Java heap space
        at edu.stanford.nlp.optimization.QNMinimizer.minimize(QNMinimizer.java:923)
        at edu.stanford.nlp.optimization.QNMinimizer.minimize(QNMinimizer.java:885)
        at edu.stanford.nlp.optimization.QNMinimizer.minimize(QNMinimizer.java:879)
        at edu.stanford.nlp.optimization.QNMinimizer.minimize(QNMinimizer.java:91)
        at edu.stanford.nlp.ie.crf.CRFClassifier.trainWeights(CRFClassifier.java:1911)
        at edu.stanford.nlp.ie.crf.CRFClassifier.train(CRFClassifier.java:1718)
        at edu.stanford.nlp.ie.AbstractSequenceClassifier.train(AbstractSequenceClassifier.java:759)
        at edu.stanford.nlp.ie.AbstractSequenceClassifier.train(AbstractSequenceClassifier.java:747)
        at edu.stanford.nlp.ie.crf.CRFClassifier.main(CRFClassifier.java:2937)