Stanford CoreNLP Java库包含一个lemmatizer ，它需要占用大量资源，但是我已经在内存小于512MB的笔记本电脑上运行了它。

要使用它：

1. 下载jar文件 ;
2. 在选择的编辑器中创建一个新项目/制作一个ant脚本，其中包括刚下载的档案中包含的所有jar文件；
3. 创建一个新的Java，如下所示（基于Stanford网站上的代码段）；

import java.util.Properties;

public class StanfordLemmatizer {

    protected StanfordCoreNLP pipeline;

    public StanfordLemmatizer() {
        // Create StanfordCoreNLP object properties, with POS tagging
        // (required for lemmatization), and lemmatization
        Properties props;
        props = new Properties();
        props.put("annotators", "tokenize, ssplit, pos, lemma");

        // StanfordCoreNLP loads a lot of models, so you probably
        // only want to do this once per execution
        this.pipeline = new StanfordCoreNLP(props);
    }

    public List<String> lemmatize(String documentText)
    {
        List<String> lemmas = new LinkedList<String>();

        // create an empty Annotation just with the given text
        Annotation document = new Annotation(documentText);

        // run all Annotators on this text
        this.pipeline.annotate(document);

        // Iterate over all of the sentences found
        List<CoreMap> sentences = document.get(SentencesAnnotation.class);
        for(CoreMap sentence: sentences) {
            // Iterate over all tokens in a sentence
            for (CoreLabel token: sentence.get(TokensAnnotation.class)) {
                // Retrieve and add the lemma for each word into the list of lemmas
                lemmas.add(token.get(LemmaAnnotation.class));
            }
        }

        return lemmas;
    }
}

克里斯对斯坦福脱胶机的回答很棒！简直美极了。他甚至包括一个指向jar文件的指针，因此我不必在Google上搜索它。

但是他的其中一行代码存在语法错误（他以某种方式在以“ lemmas.add ...”开头的行中切换了结尾的右括号和分号），但他忘记了包括导入内容。

至于NoSuchMethodError错误，通常是由于该方法没有被设为公共静态而引起的，但是如果您查看代码本身（位于http://grepcode.com/file/repo1.maven.org/maven2/com.guokr） /stan-cn-nlp/0.0.2/edu/stanford/nlp/util/Generics.java?av=h ），这不是问题。我怀疑问题出在构建路径中（我正在使用Eclipse Kepler，因此配置我在项目中使用的33个jar文件没有问题）。

以下是我对Chris代码的较小更正，并附有一个示例（我对Evanescence屠杀其完美歌词深表歉意）：

import java.util.LinkedList;
import java.util.List;
import java.util.Properties;

import edu.stanford.nlp.ling.CoreAnnotations.LemmaAnnotation;
import edu.stanford.nlp.ling.CoreAnnotations.SentencesAnnotation;
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 StanfordLemmatizer {

    protected StanfordCoreNLP pipeline;

    public StanfordLemmatizer() {
        // Create StanfordCoreNLP object properties, with POS tagging
        // (required for lemmatization), and lemmatization
        Properties props;
        props = new Properties();
        props.put("annotators", "tokenize, ssplit, pos, lemma");

        /*
         * This is a pipeline that takes in a string and returns various analyzed linguistic forms. 
         * The String is tokenized via a tokenizer (such as PTBTokenizerAnnotator), 
         * and then other sequence model style annotation can be used to add things like lemmas, 
         * POS tags, and named entities. These are returned as a list of CoreLabels. 
         * Other analysis components build and store parse trees, dependency graphs, etc. 
         * 
         * This class is designed to apply multiple Annotators to an Annotation. 
         * The idea is that you first build up the pipeline by adding Annotators, 
         * and then you take the objects you wish to annotate and pass them in and 
         * get in return a fully annotated object.
         * 
         *  StanfordCoreNLP loads a lot of models, so you probably
         *  only want to do this once per execution
         */
        this.pipeline = new StanfordCoreNLP(props);
    }

    public List<String> lemmatize(String documentText)
    {
        List<String> lemmas = new LinkedList<String>();
        // Create an empty Annotation just with the given text
        Annotation document = new Annotation(documentText);
        // run all Annotators on this text
        this.pipeline.annotate(document);
        // Iterate over all of the sentences found
        List<CoreMap> sentences = document.get(SentencesAnnotation.class);
        for(CoreMap sentence: sentences) {
            // Iterate over all tokens in a sentence
            for (CoreLabel token: sentence.get(TokensAnnotation.class)) {
                // Retrieve and add the lemma for each word into the
                // list of lemmas
                lemmas.add(token.get(LemmaAnnotation.class));
            }
        }
        return lemmas;
    }


    public static void main(String[] args) {
        System.out.println("Starting Stanford Lemmatizer");
        String text = "How could you be seeing into my eyes like open doors? \n"+
                "You led me down into my core where I've became so numb \n"+
                "Without a soul my spirit's sleeping somewhere cold \n"+
                "Until you find it there and led it back home \n"+
                "You woke me up inside \n"+
                "Called my name and saved me from the dark \n"+
                "You have bidden my blood and it ran \n"+
                "Before I would become undone \n"+
                "You saved me from the nothing I've almost become \n"+
                "You were bringing me to life \n"+
                "Now that I knew what I'm without \n"+
                "You can've just left me \n"+
                "You breathed into me and made me real \n"+
                "Frozen inside without your touch \n"+
                "Without your love, darling \n"+
                "Only you are the life among the dead \n"+
                "I've been living a lie, there's nothing inside \n"+
                "You were bringing me to life.";
        StanfordLemmatizer slem = new StanfordLemmatizer();
        System.out.println(slem.lemmatize(text));
    }

}

这是我的结果（给我留下了深刻的印象；它有时将“ s”识别为“ is”，并且几乎完成了所有其他操作）：

启动斯坦福脱胶机

添加注释器标记化

添加注释器拆分

添加注释器pos

从edu / stanford / nlp / models / pos-tagger / english-left3words / english-left3words-distsim.tagger中读取POS标记器模型...完成[1.7秒]。

添加注释器引理

[如何，您可以看到我的眼睛，像是打开门，？，您引导我向下，进入我的核心，我在哪里变得麻木没有灵魂，我的灵魂，在某个地方睡觉，冷，直到你找到那里，然后带领它，回到家，你，醒来，我，向上，在里面，叫我的名字，然后保存，我来自黑暗，你拥有，出价，我的血液，然后运行，在我消失之前，撤消，你，保存我，从，什么都没有，我几乎已经变成了，成为，带给我生命，现在，那个，我，知道了，什么，我，没有，你，可以，拥有，公正，离开，我，你，呼吸，进入，我，并使我，真实，冰冷，里面，没有，你，触摸，没有，你，爱，亲爱的，只有，你，是，生活，在其中，死者，我，有，活着，一个，说谎，，，那里，什么都没有，在里面，你，成为，带给我，生活，。