开源中文分词工具探析（六）：Stanford CoreNLP

时间 2019-11-06

标签开源中文分词工具 stanford corenlp 繁體版

原文原文链接

CoreNLP是由斯坦福大学开源的一套Java NLP工具，提供诸如：词性标注（part-of-speech (POS) tagger）、命名实体识别（named entity recognizer (NER)）、情感分析（sentiment analysis）等功能。html

【开源中文分词工具探析】系列：java

1. 前言

CoreNLP的中文分词基于CRF模型：git

\[ P_w(y|x) = \frac{exp \left( \sum_i w_i f_i(x,y) \right)}{Z_w(x)} \]github

其中，\(Z_w(x)\)为归一化因子，\(w\)为模型的参数，\(f_i(x,y)\)为特征函数。数组

2. 分解

如下源码分析基于3.7.0版本，分词示例见SegDemo类。dom

模型

主要模型文件有两份，一份为词典文件dict-chris6.ser.gz：函数

// dict-chris6.ser.gz 对应于长度为7的Set数组词典
// 共计词数：0+7323+125336+142252+82139+26907+39243
ChineseDictionary::loadDictionary(String serializePath) {
    Set<String>[] dict = new HashSet[MAX_LEXICON_LENGTH + 1];
    for (int i = 0; i <= MAX_LEXICON_LENGTH; i++) {
        dict[i] = Generics.newHashSet();
    }
    dict = IOUtils.readObjectFromURLOrClasspathOrFileSystem(serializePath);
    return dict;
}

词典的索引值为词的长度，好比第0个词典中没有词，第1个词典为长度为1的词，第6个词典为长度为6的词。其中，第6个词典为半成词，好比，有词“《双峰》（电”、“８０年国家领”、“１８２４年英”。工具

另外一份为CRF训练模型文件ctb.gz：源码分析

CRFClassifier::loadClassifier(ObjectInputStream ois, Properties props) {
    Object o = ois.readObject();
    if (o instanceof List) {
        labelIndices = (List<Index<CRFLabel>>) o; // label索引
    }
    classIndex = (Index<String>) ois.readObject(); // 序列标注label
    featureIndex = (Index<String>) ois.readObject(); // 特征
    flags = (SeqClassifierFlags) ois.readObject(); // 模型配置

    Object featureFactory = ois.readObject(); // 特征模板，用于生成特征
    else if (featureFactory instanceof FeatureFactory) {
        featureFactories = Generics.newArrayList();
        featureFactories.add((FeatureFactory<IN>) featureFactory);
    }

    windowSize = ois.readInt(); // 窗口大小为2
    weights = (double[][]) ois.readObject(); // 特征+label 对应的权重

    Set<String> lcWords = (Set<String>) ois.readObject(); // Set为空
    else {
        knownLCWords = new MaxSizeConcurrentHashSet<>(lcWords);
    }

    reinit();
}

不一样于其余分词器采用B、M、E、S四种label来作分词，CoreNLP的中文分词label只有两种，“1”表示当前字符与前一字符链接成词，“0”则表示当前字符为另外一词的开始——换言以前一字符为上一个词的结尾。测试

class CRFClassifier {
    classIndex: class edu.stanford.nlp.util.HashIndex
      ["1","0"]
}

// 中文分词label对应的类
public static class AnswerAnnotation implements CoreAnnotation<String>{}

特征

CoreNLP的特征以下（示例）：

class CRFClassifier {
    // 特征
    featureIndex: class edu.stanford.nlp.util.HashIndex
        size = 3408491
        0=的膀cc2|C
        1=身也pc|C
        44=LSSLp2spscsc2s|C
        45=科背p2p|C
        46=迪。cc2|C
        ...
        =球-行pc2|CnC
        =音非cc2|CpC
    
    // 权重
    weights: double[3408491][2]
        [[2.2114868426005005E-5, -2.2114868091546352E-5]...]
}

特征后缀只有3类：C, CpC, CnC，分别表明了三大类特征；均由特征模板生成：

// 特征模板List
featureFactories: ArrayList<FeatureFactory>
    0 = Gale2007ChineseSegmenterFeatureFactory

// 具体特征模板
Gale2007ChineseSegmenterFeatureFactory::getCliqueFeatures() {
    if (clique == cliqueC) {
        addAllInterningAndSuffixing(features, featuresC(cInfo, loc), "C");
    } else if (clique == cliqueCpC) {
        addAllInterningAndSuffixing(features, featuresCpC(cInfo, loc), "CpC");
        addAllInterningAndSuffixing(features, featuresCnC(cInfo, loc - 1), "CnC");
    }
}

特征模板只用到了两个特征簇cliqueC与cliqueCpC，其中，cliqueC由函数featuresC()实现，cliqueCpC由函数featuresCpC()与featuresCnC()

Gale2007ChineseSegmenterFeatureFactory::featuresC() {
    if (flags.useWord1) {
        // Unigram 特征
        features.add(charc +"::c"); // c[0]
        features.add(charc2+"::c2"); // c[1]
        features.add(charp +"::p"); // c[-1]
        features.add(charp2 +"::p2"); // c[-2]

        // Bigram 特征
        features.add(charc +charc2  +"::cn"); // c[0]c[1]
        features.add(charc +charc3  +"::cn2"); // c[0]c[2]
        features.add(charp +charc  +"::pc"); // c[-1]c[0]
        features.add(charp +charc2  +"::pn"); // c[-1]c[1]
        features.add(charp2 +charp  +"::p2p"); // c[-2]c[-1]
        features.add(charp2 +charc  +"::p2c"); // c[-2]c[0]
        features.add(charc2 +charc  +"::n2c"); // c[1]c[0]
    }

    // 三个字符c[-1]c[0]c[1]对应的LBeginAnnotation、LMiddleAnnotation、LEndAnnotation 三种label特征
    // 结果特征分别以6种形式结尾，"-lb", "-lm", "-le", "-plb", "-plm", "-ple", "-c2lb", "-c2lm", "-c2le"
    // null || ".../models/segmenter/chinese/dict-chris6.ser.gz"
    if (flags.dictionary != null || flags.serializedDictionary != null) {
        dictionaryFeaturesC(CoreAnnotations.LBeginAnnotation.class,
                CoreAnnotations.LMiddleAnnotation.class,
                CoreAnnotations.LEndAnnotation.class,
                "", features, p, c, c2);
    }

    // 特征 c[1]c[0], c[1]
    if (flags.useFeaturesC4gram || flags.useFeaturesC5gram || flags.useFeaturesC6gram) {
        features.add(charp2 + charp + "p2p");
        features.add(charp2 + "p2");
    }

    // Unicode特征
    if (flags.useUnicodeType || flags.useUnicodeType4gram || flags.useUnicodeType5gram) {
        features.add(uTypep + "-" + uTypec + "-" + uTypec2 + "-uType3");
    }

    // UnicodeType特征
    if (flags.useUnicodeType4gram || flags.useUnicodeType5gram) {
        features.add(uTypep2 + "-" + uTypep + "-" + uTypec + "-" + uTypec2 + "-uType4");
    }

    // UnicodeBlock特征
    if (flags.useUnicodeBlock) {
        features.add(p.getString(CoreAnnotations.UBlockAnnotation.class) + "-"
                + c.getString(CoreAnnotations.UBlockAnnotation.class) + "-"
                + c2.getString(CoreAnnotations.UBlockAnnotation.class)
                + "-uBlock");
    }

    // Shape特征
    if (flags.useShapeStrings) {
        if (flags.useShapeStrings1) {
            features.add(p.getString(CoreAnnotations.ShapeAnnotation.class) + "ps");
            features.add(c.getString(CoreAnnotations.ShapeAnnotation.class) + "cs");
            features.add(c2.getString(CoreAnnotations.ShapeAnnotation.class) + "c2s");
        }
        if (flags.useShapeStrings3) {
            features.add(p.getString(CoreAnnotations.ShapeAnnotation.class)
                    + c.getString(CoreAnnotations.ShapeAnnotation.class)
                    + c2.getString(CoreAnnotations.ShapeAnnotation.class)
                    + "pscsc2s");
        }
        if (flags.useShapeStrings4) {
            features.add(p2.getString(CoreAnnotations.ShapeAnnotation.class)
                    + p.getString(CoreAnnotations.ShapeAnnotation.class)
                    + c.getString(CoreAnnotations.ShapeAnnotation.class)
                    + c2.getString(CoreAnnotations.ShapeAnnotation.class)
                    + "p2spscsc2s");
        }
        if (flags.useShapeStrings5) {
            features.add(p2.getString(CoreAnnotations.ShapeAnnotation.class)
                    + p.getString(CoreAnnotations.ShapeAnnotation.class)
                    + c.getString(CoreAnnotations.ShapeAnnotation.class)
                    + c2.getString(CoreAnnotations.ShapeAnnotation.class)
                    + c3.getString(CoreAnnotations.ShapeAnnotation.class)
                    + "p2spscsc2sc3s");
        }
    }
}

Gale2007ChineseSegmenterFeatureFactory::featuresCpC() {}

Gale2007ChineseSegmenterFeatureFactory::featuresCnC() {}

三大类特征分别以“|C”为结尾（共计有32个）、以“|CpC”结尾（共计有37个）、以“|CnC”结尾（共计有9个）；总计78个特征。我的感受CoreNLP定义的特征过于复杂，大部分特征并无什么用。CoreNLP后面处理流程跟其余分词器别无二样了，求每一个label的权重加权之和，Viterbi解码求解最大几率路径，解析label序列获得分词结果。

CoreNLP分词速度巨慢，效果也通常，在PKU、MSR测试集上的表现以下：

测试集	分词器	准确率	召回率	F1
PKU	thulac4j	0.948	0.936	0.942
	CoreNLP	0.901	0.894	0.897
MSR	thulac4j	0.866	0.896	0.881
	CoreNLP	0.822	0.859	0.840

3.参考资料

[1] Huihsin, Tseng, et al. "A conditional random field word segmenter." Fourth SIGHAN Workshop. 2005. [2] Chang, Pi-Chuan, Michel Galley, and Christopher D. Manning. "Optimizing Chinese word segmentation for machine translation performance." Proceedings of the third workshop on statistical machine translation. Association for Computational Linguistics, 2008.