corenlp分词 stanford_开源中文分词工具探析（六）：Stanford CoreNLP
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。
【开源中文分词工具探析】系列：
1. 前言
CoreNLP的中文分词基于CRF模型：
\[
P_w(y|x) = \frac{exp \left( \sum_i w_i f_i(x,y) \right)}{Z_w(x)}
\]
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。
2. 分解
以下源码分析基于3.7.0版本，分词示例见SegDemo类。
模型
主要模型文件有两份，一份为词典文件dict-chris6.ser.gz：
// 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[] 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，比如第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，比如，有词“《双峰》(电”、“８０年国家领”、“１８２４年英”。
另一份为CRF训练模型文件/ctb.gz：
CRFClassifier::loadClassifier(ObjectInputStream ois, Properties props) {
Object o = ois.readObject();
if (o instanceof List) {
labelIndices = (List>) o; // label索引
}
classIndex = (Index) ois.readObject(); // 序列标注label
featureIndex = (Index) 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) featureFactory);
}
windowSize = ois.readInt(); // 窗口大小为2
weights = (double[][]) ois.readObject(); // 特征+label 对应的权重
Set lcWords = (Set) ois.readObject(); // Set为空
else {
knownLCWords = new MaxSizeConcurrentHashSet<>(lcWords);
}
reinit();
}
不同于其他分词器采用B、M、E、S四中label来做分词，CoreNLP的中文分词label只有两种，“1”表示当前字符与前一字符连接成词，“0”则表示当前字符为另一词的开始，换言之前一字符为上一个词的结尾。
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{}
特征
CoreNLP的特征如下(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，分别代表了三大类特征；均由特征模板生成：
// 特征模板List
featureFactories: ArrayList
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。个人感觉CoreNLP定义的特征过于复杂，大部分特征并没有什么用。
CoreNLP后面处理流程跟其他分词器别无二样了，求每个label的权重加权之和，Viterbi解码求解最大概率路径，解析label序列得到分词结果。CoreNLP分词速度巨慢，效果也一般，在PKU、MSR测试集上的表现如下：
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.