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	pos	lemma	ner");
        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+"	"+pos+"	"+lemma+"	"+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：