Stanford CoreNlp中英文Java API使用方法

       Stanford Nlp是一个比较牛叉的自然语言处理工具，其很多模型都是基于深度学习方法进行训练得到的，准确率比起原来的很多工具有了很大程度的提高。近年来很多开源项目也用到了其中的一些方法。

       最近重拾这个工具做点语义分析的工作，但是发现中文资料比较少，入门比较困难，所以整理一下自己的使用方法，希望对有需要的童鞋能够有点帮助。

       本文主要是讲如何在Java工程中调用Stanford NLP的API。

一.环境准备

       Eclipse或者IDEA，JDK1.8，Apache Maven（注意，3.5及以后的版本都需要Java8环境才能运行，如果不想在Java8运行的话，请使用以前的版本）。

       建立好一个新的Maven工程，在pom文件中加入如下代码：

    <properties>        <corenlp.version>3.6.0</corenlp.version>    </properties>    <dependencies>        <dependency>            <groupId>edu.stanford.nlp</groupId>            <artifactId>stanford-corenlp</artifactId>            <version>${corenlp.version}</version>        </dependency>        <dependency>            <groupId>edu.stanford.nlp</groupId>            <artifactId>stanford-corenlp</artifactId>            <version>${corenlp.version}</version>            <classifier>models</classifier>        </dependency>        <dependency>            <groupId>edu.stanford.nlp</groupId>            <artifactId>stanford-corenlp</artifactId>            <version>${corenlp.version}</version>            <classifier>models-chinese</classifier>        </dependency>    </dependencies>

        三个依赖包分别是CoreNlp的算法包、英文语料包、中文语料包，由于Maven默认镜像在国外，而Stanford NLP的模型文件很大，因此对网络要求比较高，网速慢的一不小心就time out下载失败了。 解决方法是找一个包含Stanford NLP依赖库的国内镜像，修改Maven的setting,xml中的mirror属性。

二.英文文本的处理

         英文的处理官网也给出了示例代码，我这里只做一下整合，代码如下：

package edu.zju.cst.krselee.examples.english;import edu.stanford.nlp.dcoref.CorefChain;import edu.stanford.nlp.dcoref.CorefCoreAnnotations;import edu.stanford.nlp.ling.CoreAnnotations;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;import edu.stanford.nlp.trees.Tree;import edu.stanford.nlp.trees.TreeCoreAnnotations;import edu.stanford.nlp.util.CoreMap;import java.util.List;import java.util.Map;import java.util.Properties;/** * Created by KrseLee on 2016/11/5. */public class StanfordEnglishNlpExample {    public static void main(String[] args) {        StanfordEnglishNlpExample example = new StanfordEnglishNlpExample();        example.runAllAnnotators();    }    public void runAllAnnotators(){        // 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 = "this is a simple text"; // 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);        parserOutput(document);    }    public void parserOutput(Annotation 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(CoreAnnotations.SentencesAnnotation.class);        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(CoreAnnotations.TokensAnnotation.class)) {                // this is the text of the token                String word = token.get(CoreAnnotations.TextAnnotation.class);                // this is the POS tag of the token                String pos = token.get(CoreAnnotations.PartOfSpeechAnnotation.class);                // this is the NER label of the token                String ne = token.get(CoreAnnotations.NamedEntityTagAnnotation.class);            }            // this is the parse tree of the current sentence            Tree tree = sentence.get(TreeCoreAnnotations.TreeAnnotation.class);            System.out.println("语法树：");            System.out.println(tree.toString());            // this is the Stanford dependency graph of the current sentence            SemanticGraph dependencies = sentence.get(SemanticGraphCoreAnnotations.CollapsedCCProcessedDependenciesAnnotation.class);            System.out.println("依存句法：");            System.out.println(dependencies.toString());        }        // 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(CorefCoreAnnotations.CorefChainAnnotation.class);    }}

     值得注意的是，Stanford NLP采用的是pipeline的方式，给用户一个参数的设置接口，之后的过程全都被封装好了，使用起来非常方便。所有的返回结果都保存在一个<pre>Annotation对象中，根据需要去获取。<a target=_blank href="http://nlp.stanford.edu/pubs/StanfordCoreNlp2014.pdf">The Stanford CoreNLP Natural Language Processing Toolkit</a> 一文中对PileLine方式做了详细的介绍，这里就不多说了，

需要提到一点就是参数中，后面的参数往往依赖于前面的参数（直观的讲，就是标注pos依赖于分词tokenize，语法分析paser依赖于标注，等等）。

三.中文文本的处理

     相对于英文来说，中文文本的处理稍微麻烦一点，主要的地方在于一个配置文件。中文语料模型包中有一个默认的配置文件
StanfordCoreNLP-chinese.properties ，
文件内容如下：
# Pipeline options - lemma is no-op for Chinese but currently needed because coref demands it (bad old requirements system)annotators = segment, ssplit, pos, lemma, ner, parse, mention, coref# segmentcustomAnnotatorClass.segment = edu.stanford.nlp.pipeline.ChineseSegmenterAnnotatorsegment.model = edu/stanford/nlp/models/segmenter/chinese/ctb.gzsegment.sighanCorporaDict = edu/stanford/nlp/models/segmenter/chinesesegment.serDictionary = edu/stanford/nlp/models/segmenter/chinese/dict-chris6.ser.gzsegment.sighanPostProcessing = true# sentence splitssplit.boundaryTokenRegex = [.]|[!?]+|[。]|[！？]+# pospos.model = edu/stanford/nlp/models/pos-tagger/chinese-distsim/chinese-distsim.tagger# nerner.model = edu/stanford/nlp/models/ner/chinese.misc.distsim.crf.ser.gzner.applyNumericClassifiers = falsener.useSUTime = false# parseparse.model = edu/stanford/nlp/models/lexparser/chineseFactored.ser.gz# corefcoref.sieves = ChineseHeadMatch, ExactStringMatch, PreciseConstructs, StrictHeadMatch1, StrictHeadMatch2, StrictHeadMatch3, StrictHeadMatch4, PronounMatchcoref.input.type = rawcoref.postprocessing = truecoref.calculateFeatureImportance = falsecoref.useConstituencyTree = truecoref.useSemantics = falsecoref.md.type = RULEcoref.mode = hybridcoref.path.word2vec =coref.language = zhcoref.print.md.log = falsecoref.defaultPronounAgreement = truecoref.zh.dict = edu/stanford/nlp/models/dcoref/zh-attributes.txt.gz


主要是指定相应pipeline的操作步骤以及对应的语料文件的位置。实际使用中我们可能用不到所有的步骤，或者要使用不同的语料库，因此可以自定义配置文件，再引入代码中。
主要的Java程序代码如下：

package edu.zju.cst.krselee.examples.chinese;import edu.stanford.nlp.dcoref.CorefChain;import edu.stanford.nlp.dcoref.CorefCoreAnnotations;import edu.stanford.nlp.ling.CoreAnnotations;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;import edu.stanford.nlp.trees.Tree;import edu.stanford.nlp.trees.TreeCoreAnnotations;import edu.stanford.nlp.util.CoreMap;import edu.stanford.nlp.util.PropertiesUtils;import edu.zju.cst.krselee.examples.english.StanfordEnglishNlpExample;import java.util.List;import java.util.Map;import java.util.Properties;/** * Created by KrseLee on 2016/11/4. */public class StanfordChineseNlpExample {    public static void main(String[] args) {        StanfordChineseNlpExample example = new StanfordChineseNlpExample();        example.runChineseAnnotators();    }    public void runChineseAnnotators(){        String text = "克林顿说，华盛顿将逐步落实对韩国的经济援助。"                + "金大中对克林顿的讲话报以掌声：克林顿总统在会谈中重申，他坚定地支持韩国摆脱经济危机。";        Annotation document = new Annotation(text);        StanfordCoreNLP corenlp = new StanfordCoreNLP("StanfordCoreNLP-chinese.properties");        corenlp.annotate(document);        parserOutput(document);    }    public void parserOutput(Annotation 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(CoreAnnotations.SentencesAnnotation.class);        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(CoreAnnotations.TokensAnnotation.class)) {                // this is the text of the token                String word = token.get(CoreAnnotations.TextAnnotation.class);                // this is the POS tag of the token                String pos = token.get(CoreAnnotations.PartOfSpeechAnnotation.class);                // this is the NER label of the token                String ne = token.get(CoreAnnotations.NamedEntityTagAnnotation.class);                System.out.println(word+"\t"+pos+"\t"+ne);            }            // this is the parse tree of the current sentence            Tree tree = sentence.get(TreeCoreAnnotations.TreeAnnotation.class);            System.out.println("语法树：");            System.out.println(tree.toString());            // this is the Stanford dependency graph of the current sentence            SemanticGraph dependencies = sentence.get(SemanticGraphCoreAnnotations.CollapsedCCProcessedDependenciesAnnotation.class);            System.out.println("依存句法：");            System.out.println(dependencies.toString());        }        // 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(CorefCoreAnnotations.CorefChainAnnotation.class);    }}

参考文献：

[1] http://stanfordnlp.github.io/CoreNLP/index.html

[2] https://blog.sectong.com/blog/corenlp_segment.html