斯坦福大学R coreNLP软件包的initCoreNLP()方法调用引发错误 [英] initCoreNLP() method call from the Stanford's R coreNLP package throws error

问题描述

我正在尝试使用coreNLP软件包.我运行了以下命令，并遇到 GC超出开销限制错误.

I am trying to use the coreNLP package. I ran the following commands and encounter the GC overhead limit exceeded error.

library(rJava)

downloadCoreNLP()

initCoreNLP()

错误是这样的:

从edu/stanford/nlp/models/ner/english.conll.4class.distsim.crf.ser.gz中加载分类器... rJava中的错误::.jnew("edu.stanford.nlp.pipeline.StanfordCoreNLP ，基本名称(路径)): java.lang.OutOfMemoryError:超出了GC开销限制 包装过程中出错:无法打开连接

Loading classifier from edu/stanford/nlp/models/ner/english.conll.4class.distsim.crf.ser.gz ... Error in rJava::.jnew("edu.stanford.nlp.pipeline.StanfordCoreNLP", basename(path)) : java.lang.OutOfMemoryError: GC overhead limit exceeded Error during wrapup: cannot open the connection

我不太了解Java，有人可以帮我吗?

I don't know much of Java, can someone help me with this?

推荐答案

@indi我遇到了同样的问题(请参阅

@indi I ran into the same problem (see R's coreNLP::initCoreNLP() throws java.lang.OutOfMemoryError) but was able to come up with a more repeatable solution than simply rebooting.

init命令的完整语法为

The full syntax for the init command is

initCoreNLP(libLoc, parameterFile, mem = "4g", annotators)

增加mem并没有帮助我，但我意识到您和我都陷入了ner注释器(命名为实体识别)中的一个分类器的困扰.由于我需要的只是词性标记，因此我将init命令替换为以下内容:

Increasing mem did not help me, but I realized that you and I were both getting stuck with one of the classifiers in the ner annotator (named entity recognition). Since all I needed was parts-of-speech tagging, I replaced the init command with the following:

initCoreNLP(mem = "8g", annotators = c("tokenize", "ssplit", "pos"))

这导致init命令在闪存中执行，并且没有内存问题.顺便说一句，我将mem增加到8g只是因为我有那么多RAM.我确定我可以将其保留为默认的4g，这样就可以了.

This caused the init command to execute in a flash and with no memory problems. BTW, I increased mem to 8g just because I have that much RAM. I'm sure I could have left it at the default 4g and it would have been fine.

我不知道您是否需要ner注释器.如果不是，则显式列出annotators参数.以下是可能值的列表: http://stanfordnlp.github.io/CoreNLP/annotators .html .只需选择完成工作所需的绝对要素即可.如果确实需要ner，则再次找出所需的最小批注，并指定它们.

I don't know if you need the ner annotator. If not, then explicitly list the annotators argument. Here is a list of the possible values: http://stanfordnlp.github.io/CoreNLP/annotators.html. Just pick the ones you absolutely need to get your job done. If you do need ner, then again figure out the minimal set of annotators you need and specify those.

所以，您(以及希望其他人)去了！

So there you (and hopefully others) go!

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