Stanford nlp 用于运行Stanford CoreNLP中文NER的代码段

我在Java程序中运行CoreNLP，使用maven依赖项。我需要在原始中文文本上运行NER。有人能提供一个代码片段来做这件事吗

我找到了说明：。。。您将首先需要运行斯坦福分词器或其他一些中文分词器，然后在该分词器的输出上运行NER！但我不知道该怎么做。你有没有把一个中文段注释员加入到英文段注释中？在此之前，您需要中文文档到EntenceProcessor吗？所有这些都可以使用StanfordCoreNLP和正确的属性集来完成，还是需要其他东西？我有中国模特

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谢谢。

您可以在中文文本上运行整个管道。关键区别在于使用段注释器而不是标记化注释器

以下是您用于整个中国管道的属性。您可以删除任何不需要的注释器。因此，在您的例子中，您可以在ner处停止并删除parse、antify和coref的属性

# Pipeline options - lemma is no-op for Chinese but currently needed because coref demands it (bad old requirements system)
annotators = segment, ssplit, pos, lemma, ner, parse, mention, coref

# segment
customAnnotatorClass.segment = edu.stanford.nlp.pipeline.ChineseSegmenterAnnotator

segment.model = edu/stanford/nlp/models/segmenter/chinese/ctb.gz
segment.sighanCorporaDict = edu/stanford/nlp/models/segmenter/chinese
segment.serDictionary = edu/stanford/nlp/models/segmenter/chinese/dict-chris6.ser.gz
segment.sighanPostProcessing = true

# sentence split
ssplit.boundaryTokenRegex = [.]|[!?]+|[。]|[！？]+

# pos
pos.model = edu/stanford/nlp/models/pos-tagger/chinese-distsim/chinese-distsim.tagger

# ner
ner.model = edu/stanford/nlp/models/ner/chinese.misc.distsim.crf.ser.gz
ner.applyNumericClassifiers = false
ner.useSUTime = false

# parse
parse.model = edu/stanford/nlp/models/lexparser/chineseFactored.ser.gz

# coref and mention
coref.sieves = ChineseHeadMatch, ExactStringMatch, PreciseConstructs, StrictHeadMatch1, StrictHeadMatch2, StrictHeadMatch3, StrictHeadMatch4, PronounMatch
coref.input.type = raw
coref.postprocessing = true
coref.calculateFeatureImportance = false
coref.useConstituencyTree = true
coref.useSemantics = false
coref.md.type = RULE
coref.mode = hybrid
coref.path.word2vec =
coref.language = zh
coref.print.md.log = false
coref.defaultPronounAgreement = true
coref.zh.dict = edu/stanford/nlp/models/dcoref/zh-attributes.txt.gz

如果有机会，我会尝试为您编写一个完整的演示类，包括适当的导入。但这是一段代码，将在中文文本上运行管道。确保类路径中有中文模型jar。您可以学习如何在Maven中添加中文模型jar

Properties props = new Properties();
props = StringUtils.propFileToProperties("StanfordCoreNLP-chinese.properties");
// that properties file will run the entire pipeline
// if you uncomment the following line it will just go up to ner
//props.setProperty("annotators","segment,ssplit,pos,lemma,ner");
StanfordCoreNLP pipeline = new StanfordCoreNLP(props);
Annotation annotation = new Annotation("whatever your Chinese text is");
pipeline.annotate(annotation);

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您可以在中文文本上运行整个管道。关键区别在于使用段注释器而不是标记化注释器

以下是您用于整个中国管道的属性。您可以删除任何不需要的注释器。因此，在您的例子中，您可以在ner处停止并删除parse、antify和coref的属性

# Pipeline options - lemma is no-op for Chinese but currently needed because coref demands it (bad old requirements system)
annotators = segment, ssplit, pos, lemma, ner, parse, mention, coref

# segment
customAnnotatorClass.segment = edu.stanford.nlp.pipeline.ChineseSegmenterAnnotator

segment.model = edu/stanford/nlp/models/segmenter/chinese/ctb.gz
segment.sighanCorporaDict = edu/stanford/nlp/models/segmenter/chinese
segment.serDictionary = edu/stanford/nlp/models/segmenter/chinese/dict-chris6.ser.gz
segment.sighanPostProcessing = true

# sentence split
ssplit.boundaryTokenRegex = [.]|[!?]+|[。]|[！？]+

# pos
pos.model = edu/stanford/nlp/models/pos-tagger/chinese-distsim/chinese-distsim.tagger

# ner
ner.model = edu/stanford/nlp/models/ner/chinese.misc.distsim.crf.ser.gz
ner.applyNumericClassifiers = false
ner.useSUTime = false

# parse
parse.model = edu/stanford/nlp/models/lexparser/chineseFactored.ser.gz

# coref and mention
coref.sieves = ChineseHeadMatch, ExactStringMatch, PreciseConstructs, StrictHeadMatch1, StrictHeadMatch2, StrictHeadMatch3, StrictHeadMatch4, PronounMatch
coref.input.type = raw
coref.postprocessing = true
coref.calculateFeatureImportance = false
coref.useConstituencyTree = true
coref.useSemantics = false
coref.md.type = RULE
coref.mode = hybrid
coref.path.word2vec =
coref.language = zh
coref.print.md.log = false
coref.defaultPronounAgreement = true
coref.zh.dict = edu/stanford/nlp/models/dcoref/zh-attributes.txt.gz

如果有机会，我会尝试为您编写一个完整的演示类，包括适当的导入。但这是一段代码，将在中文文本上运行管道。确保类路径中有中文模型jar。您可以学习如何在Maven中添加中文模型jar

Properties props = new Properties();
props = StringUtils.propFileToProperties("StanfordCoreNLP-chinese.properties");
// that properties file will run the entire pipeline
// if you uncomment the following line it will just go up to ner
//props.setProperty("annotators","segment,ssplit,pos,lemma,ner");
StanfordCoreNLP pipeline = new StanfordCoreNLP(props);
Annotation annotation = new Annotation("whatever your Chinese text is");
pipeline.annotate(annotation);

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好吧，我放弃了，为什么这个值得a-1？好吧，我放弃了，为什么这个值得a-1？非常有用！！非常感谢您的帮助！！深切感激