Stanford CoreNLP由Java语言开发，是斯坦福大学自然语言处理小组的研究成果。
包含自然语言处理的基本任务：分词、词性标注、依存句法分析、命名实体识别等等，支持多语言。项目地址：GitHub

本文主要记录使用注意事项。

##Stage 1
首先我们要下载 CoreNLP Java包。

然后在Eclipse里面的Classpath里面引入jar文件。如下图红色框所示：

CoreNLP只需要引入jar文件就可以了，不用再引入其他文件。

然后代码送上：

package ...

import java.util.ArrayList;
import java.util.List;
import java.util.Properties;

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.util.CoreMap;

public class NlpUtil {
	public static void main(String[] args) {
		// creates a StanfordCoreNLP object, with POS tagging, lemmatization,
		// NER, parsing, and coreference resolution
		Properties props = new Properties();
		props.setProperty("annotators", "tokenize, ssplit, pos, lemma, ner, parse, dcoref");
		StanfordCoreNLP pipeline = new StanfordCoreNLP(props);

		// read some text in the text variable
		String text = "She went to America last week.";// Add your text here!
		// 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);

		List<String> words = new ArrayList<>();
		List<String> posTags = new ArrayList<>();
		List<String> nerTags = new ArrayList<>();
		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);
				words.add(word);
				// this is the POS tag of the token
				String pos = token.get(PartOfSpeechAnnotation.class);
				posTags.add(pos);
				// this is the NER label of the token
				String ne = token.get(NamedEntityTagAnnotation.class);
				nerTags.add(ne);
			}
		}
		
		System.out.println(words.toString());
		System.out.println(posTags.toString());
		System.out.println(nerTags.toString());

	}

}

输出：
[She, went, to, America, last, week, .]
[PRP, VBD, TO, NNP, JJ, NN, .]
[O, O, O, LOCATION, DATE, DATE, O]

就这样愉快的搞定。

##Stage 2
然而…
上面说的都是默认配置，默认是处理英文的，如果要进行中文处理，则还需要引入一个jar文件，下载stanford-chinese-corenlp-2016-01-19-models.jar：

Classpath：

然后代码送上：

package ...

import edu.stanford.nlp.pipeline.Annotation;
import edu.stanford.nlp.pipeline.StanfordCoreNLP;

public class NlpUtil {
	public static void main(String[] args) {

		String text = "马飚向伦古转达了***主席的诚挚祝贺和良好祝愿。马飚表示，中国和赞比亚建交50多年来，双方始终真诚友好、平等相待，友好合作结出累累硕果，给两国人民带来了实实在在的利益。中方高度重视中赞关系发展，愿以落实两国元首共识和中非合作论坛约翰内斯堡峰会成果为契机，推动中赞关系再上新台阶。";
		Annotation document = new Annotation(text);
		StanfordCoreNLP corenlp = new StanfordCoreNLP("StanfordCoreNLP-chinese.properties");
		corenlp.annotate(document);

		corenlp.prettyPrint(document, System.out);

	}

}

部分输出（新窗口打开可查看大图）：

处理中文时经常出现OutOfMemoryError异常，设置运行时虚拟机堆大小就好了：

就这样，用Stanford CoreNLP处理中文也愉快地搞定了。