【发布时间】:2016-06-28 10:00:05
【问题描述】:
我一直在使用属性文件训练我的 ner 模型,如教程LINK 中所示。我正在使用相同的道具文件,但是当我无法理解如何以编程方式执行它时。
props.setProperty("annotators", "tokenize, ssplit, pos, lemma, ner, parse, sentiment, regexner");
props.setProperty("ner.model", "resources/NER.prop");
prop文件如下:
# location of the training file
trainFile = nerTEST.tsv
# 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 = resources/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
# 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
错误:
java.io.StreamCorruptedException: invalid stream header: 23206C6F
....
..
Caused by: java.io.IOException: Couldn't load classifier from resources/NER.prop
从SO 的另一个问题,我了解到您直接提供模型文件。但是,我们如何在属性文件的帮助下做到这一点呢?
【问题讨论】:
标签: java named-entity-recognition stanford-nlp