Java 执行和测试 stanford core nlp 示例

Executing and testing stanford core nlp example

提问by user123

I downloaded stanford core nlp packages and tried to test it on my machine.

我下载了 stanford core nlp 包并尝试在我的机器上测试它。

Using command: java -cp "*" -mx1g edu.stanford.nlp.sentiment.SentimentPipeline -file input.txt

使用命令： java -cp "*" -mx1g edu.stanford.nlp.sentiment.SentimentPipeline -file input.txt

I got sentiment result in form of positiveor negative. input.txtcontains the sentence to be tested.

我得到了形式为positiveor 的情绪结果negative。input.txt包含要测试的句子。

On more command: java -cp stanford-corenlp-3.3.0.jar;stanford-corenlp-3.3.0-models.jar;xom.jar;joda-time.jar -Xmx600m edu.stanford.nlp.pipeline.StanfordCoreNLP -annotators tokenize,ssplit,pos,lemma,parse -file input.txtwhen executed gives follwing lines :

在更多命令上：java -cp stanford-corenlp-3.3.0.jar;stanford-corenlp-3.3.0-models.jar;xom.jar;joda-time.jar -Xmx600m edu.stanford.nlp.pipeline.StanfordCoreNLP -annotators tokenize,ssplit,pos,lemma,parse -file input.txt执行时给出以下行：

H:\Drive E\Stanford\stanfor-corenlp-full-2013~>java -cp stanford-corenlp-3.3.0.j
ar;stanford-corenlp-3.3.0-models.jar;xom.jar;joda-time.jar -Xmx600m edu.stanford
.nlp.pipeline.StanfordCoreNLP -annotators tokenize,ssplit,pos,lemma,parse -file
input.txt
Adding annotator tokenize
Adding annotator ssplit
Adding annotator pos
Reading POS tagger model from edu/stanford/nlp/models/pos-tagger/english-left3wo
rds/english-left3words-distsim.tagger ... done [36.6 sec].
Adding annotator lemma
Adding annotator parse
Loading parser from serialized file edu/stanford/nlp/models/lexparser/englishPCF
G.ser.gz ... done [13.7 sec].

Ready to process: 1 files, skipped 0, total 1
Processing file H:\Drive E\Stanford\stanfor-corenlp-full-2013~\input.txt ... wri
ting to H:\Drive E\Stanford\stanfor-corenlp-full-2013~\input.txt.xml {
  Annotating file H:\Drive E\Stanford\stanfor-corenlp-full-2013~\input.txt [13.6
81 seconds]
} [20.280 seconds]
Processed 1 documents
Skipped 0 documents, error annotating 0 documents
Annotation pipeline timing information:
PTBTokenizerAnnotator: 0.4 sec.
WordsToSentencesAnnotator: 0.0 sec.
POSTaggerAnnotator: 1.8 sec.
MorphaAnnotator: 2.2 sec.
ParserAnnotator: 9.1 sec.
TOTAL: 13.6 sec. for 10 tokens at 0.7 tokens/sec.
Pipeline setup: 58.2 sec.
Total time for StanfordCoreNLP pipeline: 79.6 sec.

H:\Drive E\Stanford\stanfor-corenlp-full-2013~>

Could understand. No informative result.

可以理解。没有信息性结果。

I got one example at : stanford core nlp java output

我有一个例子：stanford core nlp java output

import java.io.*;
import java.util.*;

import edu.stanford.nlp.io.*;
import edu.stanford.nlp.ling.*;
import edu.stanford.nlp.pipeline.*;
import edu.stanford.nlp.trees.*;
import edu.stanford.nlp.util.*;

public class StanfordCoreNlpDemo {

  public static void main(String[] args) throws IOException {
    PrintWriter out;
    if (args.length > 1) {
      out = new PrintWriter(args[1]);
    } else {
      out = new PrintWriter(System.out);
    }
    PrintWriter xmlOut = null;
    if (args.length > 2) {
      xmlOut = new PrintWriter(args[2]);
    }

    StanfordCoreNLP pipeline = new StanfordCoreNLP();
    Annotation annotation;
    if (args.length > 0) {
      annotation = new Annotation(IOUtils.slurpFileNoExceptions(args[0]));
    } else {
      annotation = new Annotation("Kosgi Santosh sent an email to Stanford University. He didn't get a reply.");
    }

    pipeline.annotate(annotation);
    pipeline.prettyPrint(annotation, out);
    if (xmlOut != null) {
      pipeline.xmlPrint(annotation, xmlOut);
    }
    // An Annotation is a Map and you can get and use the various analyses individually.
    // For instance, this gets the parse tree of the first sentence in the text.
    List<CoreMap> sentences = annotation.get(CoreAnnotations.SentencesAnnotation.class);
    if (sentences != null && sentences.size() > 0) {
      CoreMap sentence = sentences.get(0);
      Tree tree = sentence.get(TreeCoreAnnotations.TreeAnnotation.class);
      out.println();
      out.println("The first sentence parsed is:");
      tree.pennPrint(out);
    }
  }

}

Tried to execute it in netbeans with including necessary library. But it always stuck in between or gives exception Exception in thread “main” java.lang.OutOfMemoryError: Java heap space

尝试在包含必要库的 netbeans 中执行它。但它总是卡在两者之间或给出例外Exception in thread “main” java.lang.OutOfMemoryError: Java heap space

Thou I set the memory to be allocated in property/run/VM box

你我设置了要分配的内存 property/run/VM box

Any idea how can I run above java example using command line?

知道如何使用命令行在 java 示例之上运行吗？

I want to get sentiment score of the example

我想获得示例的情绪分数

UPDATE

更新

output of : java -cp "*" -mx1g edu.stanford.nlp.sentiment.SentimentPipeline -file input.txt

的输出： java -cp "*" -mx1g edu.stanford.nlp.sentiment.SentimentPipeline -file input.txt

out put of: java -cp stanford-corenlp-3.3.0.j ar;stanford-corenlp-3.3.0-models.jar;xom.jar;joda-time.jar -Xmx600m edu.stanford .nlp.pipeline.StanfordCoreNLP -annotators tokenize,ssplit,pos,lemma,parse -file input.txt

输出： java -cp stanford-corenlp-3.3.0.j ar;stanford-corenlp-3.3.0-models.jar;xom.jar;joda-time.jar -Xmx600m edu.stanford .nlp.pipeline.StanfordCoreNLP -annotators tokenize,ssplit,pos,lemma,parse -file input.txt

采纳答案by lababidi

You need to add the "sentiment" annotator to the list of annotators:

您需要将“情绪”注释器添加到注释器列表中：

-annotators tokenize,ssplit,pos,lemma,parse,sentiment

This will add a "sentiment" property to each sentence node in your XML.

这将为 XML 中的每个句子节点添加一个“情绪”属性。

回答by Elliott Frisch

Per the example hereyou need to run the Sentiment Analysis.

根据此处的示例，您需要运行情绪分析。

java -cp "*" -mx5g edu.stanford.nlp.sentiment.SentimentPipeline -file input.txt

Apparently this is a memory expensive operation, it may not complete with only 1 gigabyte. Then you can use the "Evaluation Tool"

显然这是一个内存昂贵的操作，它可能无法仅用 1 GB 来完成。然后你可以使用“评估工具”

java -cp "*" edu.stanford.nlp.sentiment.Evaluate edu/stanford/nlp/models/sentiment/sentiment.ser.gz input.txt

回答by saganas

You can do the following in your code:

您可以在代码中执行以下操作：

String text = "I am feeling very sad and frustrated.";
Properties props = new Properties();
props.setProperty("annotators", "tokenize, ssplit, pos, lemma, parse, sentiment");
StanfordCoreNLP pipeline = new StanfordCoreNLP(props);
<...>
Annotation annotation = pipeline.process(text);
List<CoreMap> sentences = annotation.get(CoreAnnotations.SentencesAnnotation.class);
for (CoreMap sentence : sentences) {
  String sentiment = sentence.get(SentimentCoreAnnotations.SentimentClass.class);
  System.out.println(sentiment + "\t" + sentence);
}

It will print the sentiment of the sentence and the sentence itself, e.g. "I am feeling very sad and frustrated.":

它将打印句子的情绪和句子本身，例如“我感到非常悲伤和沮丧。”：

Negative    I am feeling very sad and frustrated.

回答by 53by97

This is working fine for me -

这对我来说很好用 -

Maven Dependencies :

Maven 依赖项：

        <dependency>
            <groupId>edu.stanford.nlp</groupId>
            <artifactId>stanford-corenlp</artifactId>
            <version>3.5.2</version>
            <classifier>models</classifier>
        </dependency>
        <dependency>
            <groupId>edu.stanford.nlp</groupId>
            <artifactId>stanford-corenlp</artifactId>
            <version>3.5.2</version>
        </dependency>
        <dependency>
            <groupId>edu.stanford.nlp</groupId>
            <artifactId>stanford-parser</artifactId>
            <version>3.5.2</version>
        </dependency>

Java Code :

代码：

public static void main(String[] args) throws IOException {
        String text = "This World is an amazing place";
        Properties props = new Properties();
        props.setProperty("annotators", "tokenize, ssplit, pos, lemma, parse, sentiment");
        StanfordCoreNLP pipeline = new StanfordCoreNLP(props);

        Annotation annotation = pipeline.process(text);
        List<CoreMap> sentences = annotation.get(CoreAnnotations.SentencesAnnotation.class);
        for (CoreMap sentence : sentences) {
            String sentiment = sentence.get(SentimentCoreAnnotations.SentimentClass.class);
            System.out.println(sentiment + "\t" + sentence);
        }
    }

Results :

结果 ：

Very positive This World is an amazing place

非常积极 这个世界是一个了不起的地方