本文的目的是介绍StanfordNLPforPython的详细情况，我们将通过专业的研究、有关数据的分析等多种方式，同时也不会遗漏关于Eclipse下使用StanfordCoreNLP的方法、Ecli

本文的目的是介绍Stanford NLP for Python的详细情况，我们将通过专业的研究、有关数据的分析等多种方式，同时也不会遗漏关于Eclipse 下使用 Stanford CoreNLP 的方法、Eclipse 编译StanfordNLP、HTML – 我还可以使用什么来代替htp.print和dbms_output on to to to for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for、Java Stanford NLP：语音标签的一部分？的知识。

• Stanford NLP for Python
• Eclipse 下使用 Stanford CoreNLP 的方法
• Eclipse 编译StanfordNLP
• HTML – 我还可以使用什么来代替htp.print和dbms_output on to to to for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for
• Java Stanford NLP：语音标签的一部分？

Stanford NLP for Python

Eclipse 下使用 Stanford CoreNLP 的方法

源码下载地址：CoreNLP 官网。

目前 release 的 CoreNLP version 3.5.0 版本仅支持 java-1.8 及以上版本，因此有时需要为 Eclipse 添加 jdk-1.8 配置，配置方法如下：

• 首先，去 oracle 官网下载 java-1.8，下载网址为：java 下载，安装完成后。
• 打开 Eclipse，选择 Window -> Preferences -> Java –> Installed JREs 进行配置：
• 点击窗体右边的“add”，然后添加一个“Standard VM”（应该是标准虚拟机的意思），然后点击“next”；
• 在”JRE HOME”那一行点击右边的“Directory…”找到你java 的安装路径，比如“C:Program Files/Java/jdk1.8”

这样你的 Eclipse 就已经支持 jdk-1.8 了。

1. 新建 java 工程，注意编译环境版本选择 1.8

2. 将官网下载的源码解压到工程下，并导入所需 jar 包

如导入 stanford-corenlp-3.5.0.jar、stanford-corenlp-3.5.0-javadoc.jar、stanford-corenlp-3.5.0-models.jar、stanford-corenlp-3.5.0-sources.jar、xom.jar 等

导入 jar 包过程为：项目右击 ->Properties->Java Build Path->Libraries，点击 “Add JARs”，在路径中选取相应的 jar 包即可。

3. 新建 TestCoreNLP 类，代码如下

package Test;

import java.util.List;
import java.util.Map;
import java.util.Properties;

import edu.stanford.nlp.dcoref.CorefChain;
import edu.stanford.nlp.dcoref.CorefCoreAnnotations.CorefChainAnnotation;
import edu.stanford.nlp.ling.CoreAnnotations.LemmaAnnotation;
import edu.stanford.nlp.ling.CoreAnnotations.NamedEntityTagAnnotation;
import edu.stanford.nlp.ling.CoreAnnotations.PartOfSpeechAnnotation;
import edu.stanford.nlp.ling.CoreAnnotations.SentencesAnnotation;
import edu.stanford.nlp.ling.CoreAnnotations.TextAnnotation;
import edu.stanford.nlp.ling.CoreAnnotations.TokensAnnotation;
import edu.stanford.nlp.ling.CoreLabel;
import edu.stanford.nlp.pipeline.Annotation;
import edu.stanford.nlp.pipeline.StanfordCoreNLP;
import edu.stanford.nlp.semgraph.SemanticGraph;
import edu.stanford.nlp.semgraph.SemanticGraphCoreAnnotations.CollapsedCCProcessedDependenciesAnnotation;
import edu.stanford.nlp.sentiment.SentimentCoreAnnotations;
import edu.stanford.nlp.trees.Tree;
import edu.stanford.nlp.trees.TreeCoreAnnotations.TreeAnnotation;
import edu.stanford.nlp.util.CoreMap;

public class TestCoreNLP {
    public static void main(String[] args) {
        // creates a StanfordCoreNLP object, with POS tagging, lemmatization, NER, parsing, and coreference resolution
        Properties props = new Properties();
        props.put("annotators", "tokenize, ssplit, pos, lemma, ner, parse, dcoref");
        StanfordCoreNLP pipeline = new StanfordCoreNLP(props);
        
        // read some text in the text variable
        String text = "Add your text here:Beijing sings Lenovo";
        
        // create an empty Annotation just with the given text
        Annotation document = new Annotation(text);
        
        // run all Annotators on this text
        pipeline.annotate(document);
        
        // these are all the sentences in this document
        // a CoreMap is essentially a Map that uses class objects as keys and has values with custom types
        List<CoreMap> sentences = document.get(SentencesAnnotation.class);
        
        System.out.println("word\tpos\tlemma\tner");
        for(CoreMap sentence: sentences) {
             // traversing the words in the current sentence
             // a CoreLabel is a CoreMap with additional token-specific methods
            for (CoreLabel token: sentence.get(TokensAnnotation.class)) {
                // this is the text of the token
                String word = token.get(TextAnnotation.class);
                // this is the POS tag of the token
                String pos = token.get(PartOfSpeechAnnotation.class);
                // this is the NER label of the token
                String ne = token.get(NamedEntityTagAnnotation.class);
                String lemma = token.get(LemmaAnnotation.class);
                
                System.out.println(word+"\t"+pos+"\t"+lemma+"\t"+ne);
            }
            // this is the parse tree of the current sentence
            Tree tree = sentence.get(TreeAnnotation.class);
            
            // this is the Stanford dependency graph of the current sentence
            SemanticGraph dependencies = sentence.get(CollapsedCCProcessedDependenciesAnnotation.class);
        }
        // This is the coreference link graph
        // Each chain stores a set of mentions that link to each other,
        // along with a method for getting the most representative mention
        // Both sentence and token offsets start at 1!
        Map<Integer, CorefChain> graph = document.get(CorefChainAnnotation.class);
    }
}

PS：该代码的思想是将 text 字符串交给 Stanford CoreNLP 处理，StanfordCoreNLP 的各个组件（annotator）按 “tokenize（分词）, ssplit（断句）, pos（词性标注）, lemma（词元化）, ner（命名实体识别）, parse（语法分析）, dcoref（同义词分辨）” 顺序进行处理。

处理完后 List<CoreMap> sentences = document.get (SentencesAnnotation.class); 中包含了所有分析结果，遍历即可获知结果。

这里简单的将单词、词性、词元、是否实体打印出来。其余的用法参见官网（如 sentiment、parse、relation 等）。

4. 执行结果：

5. 关于 CoreNLP 详细内容：

http://stanfordnlp.github.io/CoreNLP/

Eclipse 编译StanfordNLP

1、源码最新下载地址：http://nlp.stanford.edu/software/index.shtml;

2、解压stanford-corenlp.zip;

3、打开Eclipse新建JAVA项目stanford-corenlp，然后在项目根目录创建文夹名"lib";

4、将步骤2解压出的*.jar复制到刚才新建lib目录中；stanford-corenlp-3.3.1-sources源代码解压至项目目录src里后，刷新项目；

5、将lib中*.jar文件添加引用；

6、将步骤2解压文件中的input.txt拷贝到工程根目录下，图片参上，项目编译完成后input.txt.xml会自动产生；

7、编译器版本应该大于等于1.6否则会出错；

8、编译成功提示如下：

可参考地址：

http://nlp.stanford.edu/software/index.shtml

http://blog.csdn.net/jdbc/article/details/50494622

HTML – 我还可以使用什么来代替htp.print和dbms_output on to to to for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for

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Java Stanford NLP：语音标签的一部分？

Colorless/JJ green/JJ ideas/NNS sleep/VBP furiously/RB ./.

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