失眠网 > 使用Standford coreNLP进行中文命名实体识别(NER)

使用Standford coreNLP进行中文命名实体识别(NER)

时间：2023-01-07 23:37:26

Stanford CoreNLP是一个比较厉害的自然语言处理工具，很多模型都是基于深度学习方法训练得到的。

先附上其官网链接：

https://stanfordnlp.github.io/CoreNLP/index.htmlhttps://nlp.stanford.edu/nlp/javadoc/javanlp//stanfordnlp/CoreNLP

本文主要讲解如何在java工程中使用Stanford CoreNLP；

1.环境准备

3.5之后的版本都需要java8以上的环境才能运行。需要进行中文处理的话，比较占用内存，3G左右的内存消耗。

笔者使用的maven进行依赖的引入，使用的是3.9.1版本。

直接在pom文件中加入下面的依赖：

<dependency><groupId>edu.stanford.nlp</groupId><artifactId>stanford-corenlp</artifactId><version>3.9.2</version></dependency><dependency><groupId>edu.stanford.nlp</groupId><artifactId>stanford-corenlp</artifactId><version>3.9.2</version><classifier>models</classifier></dependency><dependency><groupId>edu.stanford.nlp</groupId><artifactId>stanford-corenlp</artifactId><version>3.9.2</version><classifier>models-chinese</classifier></dependency>

3个包分别是CoreNLP的算法包、英文语料包、中文预料包。这3个包的总大小为1.43G。maven默认镜像在国外，而这几个依赖包特别大，可以找有着三个依赖的国内镜像试一下。笔者用的是自己公司的maven仓库。

2.代码调用

需要注意的是，因为我是需要进行中文的命名实体识别，因此需要使用中文分词和中文的词典。

其中有个StanfordCoreNLP-chinese.properties文件，这里面设定了进行中文自然语言处理的一些参数。主要指定相应的pipeline的操作步骤以及对应的预料文件的位置。实际上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，然后再引入。那在我的项目中，我就直接读取了该properties文件。

attention：此处笔者要使用的是ner功能，但可能不想使用其他的一些annotation，想去掉。然而，Stanford CoreNLP有一些局限，就是在ner执行之前，一定需要tokenize, ssplit, pos, lemma的引入，当然这增加了很大的时间耗时。

其实我们可以先来分析一下这个properties文件：

# Pipeline options - lemma is no-op for Chinese but currently needed because coref demands it (bad old requirements system)annotators = tokenize, ssplit, pos, lemma, ner, parse, coref# segmenttokenize.language = zhsegment.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# ner 此处设定了ner使用的语言、模型（crf），目前SUTime只支持英文，不支持中文，所以设置为false。ner.language = chinesener.model = edu/stanford/nlp/models/ner/chinese.misc.distsim.crf.ser.gzner.applyNumericClassifiers = truener.useSUTime = false# regexnerner.fine.regexner.mapping = edu/stanford/nlp/models/kbp/chinese/cn_regexner_mapping.tabner.fine.regexner.noDefaultOverwriteLabels = CITY,COUNTRY,STATE_OR_PROVINCE# parseparse.model = edu/stanford/nlp/models/srparser/chineseSR.ser.gz# depparsedepparse.model = edu/stanford/nlp/models/parser/nndep/UD_Chinese.gzdepparse.language = chinese# corefcoref.sieves = ChineseHeadMatch, ExactStringMatch, PreciseConstructs, StrictHeadMatch1, StrictHeadMatch2, StrictHeadMatch3, StrictHeadMatch4, PronounMatchcoref.input.type = rawcoref.postprocessing = truecoref.calculateFeatureImportance = falsecoref.useConstituencyTree = truecoref.useSemantics = falsecoref.algorithm = hybridcoref.path.word2vec =coref.language = zhcoref.defaultPronounAgreement = truecoref.zh.dict = edu/stanford/nlp/models/dcoref/zh-attributes.txt.gzcoref.print.md.log = falsecoref.md.type = RULEcoref.md.liberalChineseMD = false# kbpkbp.semgrex = edu/stanford/nlp/models/kbp/chinese/semgrexkbp.tokensregex = edu/stanford/nlp/models/kbp/chinese/tokensregexkbp.language = zhkbp.model = none# entitylinkentitylink.wikidict = edu/stanford/nlp/models/kbp/chinese/wikidict_chinese.tsv.gz

那我们就直接在代码中引入这个properties文件，参考代码如下：

package com.baidu.corenlp;import java.util.List;import java.util.Map;import java.util.Properties;import edu.stanford.nlp.coref.CorefCoreAnnotations;import edu.stanford.nlp.coref.data.CorefChain;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;/*** Created by sonofelice on /3/27.*/public class TestNLP {public void test() throws Exception {//构造一个StanfordCoreNLP对象，配置NLP的功能，如lemma是词干化，ner是命名实体识别等Properties props = new Properties();props.load(this.getClass().getResourceAsStream("/StanfordCoreNLP-chinese.properties"));StanfordCoreNLP pipeline = new StanfordCoreNLP(props);String text = "袁隆平是中国科学院的院士,他于10月到中国山东省东营市东营区永乐机场附近承包了一千亩盐碱地,"+ "开始种植棉花, 年产量达到一万吨, 哈哈, 反正棣琦说的是假的,逗你玩儿,明天下午2点来我家吃饭吧。"+ "棣琦是山东大学毕业的,目前在百度做java开发,位置是东北旺东路102号院,手机号14366778890";long startTime = System.currentTimeMillis();// 创造一个空的Annotation对象Annotation document = new Annotation(text);// 对文本进行分析pipeline.annotate(document);//获取文本处理结果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 methodsfor (CoreLabel token : sentence.get(CoreAnnotations.TokensAnnotation.class)) {//// 获取句子的token（可以是作为分词后的词语）String word = token.get(CoreAnnotations.TextAnnotation.class);System.out.println(word);//词性标注String pos = token.get(CoreAnnotations.PartOfSpeechAnnotation.class);System.out.println(pos);// 命名实体识别String ne = token.get(CoreAnnotations.NormalizedNamedEntityTagAnnotation.class);String ner = token.get(CoreAnnotations.NamedEntityTagAnnotation.class);System.out.println(word + " | analysis : { original : " + ner + "," + " normalized : "+ ne + "}");//词干化处理String lema = token.get(CoreAnnotations.LemmaAnnotation.class);System.out.println(lema);}// 句子的解析树Tree tree = sentence.get(TreeCoreAnnotations.TreeAnnotation.class);System.out.println("句子的解析树:");tree.pennPrint();// 句子的依赖图SemanticGraph graph =sentence.get(SemanticGraphCoreAnnotations.CollapsedCCProcessedDependenciesAnnotation.class);System.out.println("句子的依赖图");System.out.println(graph.toString(SemanticGraph.OutputFormat.LIST));}long endTime = System.currentTimeMillis();long time = endTime - startTime;System.out.println("The analysis lasts " + time + " seconds * 1000");// 指代词链//每条链保存指代的集合// 句子和偏移量都从1开始Map<Integer, CorefChain> corefChains = document.get(CorefCoreAnnotations.CorefChainAnnotation.class);if (corefChains == null) {return;}for (Map.Entry<Integer, CorefChain> entry : corefChains.entrySet()) {System.out.println("Chain " + entry.getKey() + " ");for (CorefChain.CorefMention m : entry.getValue().getMentionsInTextualOrder()) {// We need to subtract one since the indices count from 1 but the Lists start from 0List<CoreLabel> tokens = sentences.get(m.sentNum - 1).get(CoreAnnotations.TokensAnnotation.class);// We subtract two for end: one for 0-based indexing, and one because we want last token of mention // not one following.System.out.println(" " + m + ", i.e., 0-based character offsets [" + tokens.get(m.startIndex - 1).beginPosition()+", " + tokens.get(m.endIndex - 2).endPosition() + ")");}}}}public static void main(String[] args) throws Exception {TestNLP nlp=new TestNLP();nlp.test();}

当然，我在运行过程中，只保留了ner相关的分析，别的功能注释掉了。输出结果如下：

19:46:16.000 [main] INFO e.s.nlp.pipeline.StanfordCoreNLP - Adding annotator pos19:46:19.387 [main] INFO e.s.nlp.tagger.maxent.MaxentTagger - Loading POS tagger from edu/stanford/nlp/models/pos-tagger/chinese-distsim/chinese-distsim.tagger ... done [3.4 sec].19:46:19.388 [main] INFO e.s.nlp.pipeline.StanfordCoreNLP - Adding annotator lemma19:46:19.389 [main] INFO e.s.nlp.pipeline.StanfordCoreNLP - Adding annotator ner19:46:21.938 [main] INFO e.s.n.ie.AbstractSequenceClassifier - Loading classifier from edu/stanford/nlp/models/ner/chinese.misc.distsim.crf.ser.gz ... done [2.5 sec].19:46:22.099 [main] WARN e.s.n.p.TokensRegexNERAnnotator - TokensRegexNERAnnotator ner.fine.regexner: Entry has multiple types for ner: 巴伐利亚 STATE_OR_PROVINCE MISC,GPE,LOCATION 1. Taking type to be MISC19:46:22.100 [main] WARN e.s.n.p.TokensRegexNERAnnotator - TokensRegexNERAnnotator ner.fine.regexner: Entry has multiple types for ner: 巴伐利亚州 STATE_OR_PROVINCE MISC,GPE,LOCATION 1. Taking type to be MISC19:46:22.100 [main] INFO e.s.n.p.TokensRegexNERAnnotator - TokensRegexNERAnnotator ner.fine.regexner: Read 21238 unique entries out of 21249 from edu/stanford/nlp/models/kbp/chinese/cn_regexner_mapping.tab, 0 TokensRegex patterns.19:46:22.532 [main] INFO e.s.nlp.pipeline.StanfordCoreNLP - Adding annotator parse19:46:35.855 [main] INFO e.s.mon.ParserGrammar - Loading parser from serialized file edu/stanford/nlp/models/srparser/chineseSR.ser.gz ... done [13.3 sec].19:46:35.859 [main] INFO e.s.nlp.pipeline.StanfordCoreNLP - Adding annotator coref19:46:43.139 [main] INFO e.s.n.pipeline.CorefMentionAnnotator - Using mention detector type: rule19:46:43.148 [main] INFO e.s.nlp.wordseg.ChineseDictionary - Loading Chinese dictionaries from 1 file:19:46:43.148 [main] INFO e.s.nlp.wordseg.ChineseDictionary - edu/stanford/nlp/models/segmenter/chinese/dict-chris6.ser.gz19:46:43.329 [main] INFO e.s.nlp.wordseg.ChineseDictionary - Done. Unique words in ChineseDictionary is: 423200.19:46:43.379 [main] INFO edu.stanford.nlp.wordseg.CorpusChar - Loading character dictionary file from edu/stanford/nlp/models/segmenter/chinese/dict/character_list [done].19:46:43.380 [main] INFO e.s.nlp.wordseg.AffixDictionary - Loading affix dictionary from edu/stanford/nlp/models/segmenter/chinese/dict/in.ctb [done].袁隆平 | analysis : { original : PERSON, normalized : null}是 | analysis : { original : O, normalized : null}中国 | analysis : { original : ORGANIZATION, normalized : null}科学院 | analysis : { original : ORGANIZATION, normalized : null}的 | analysis : { original : O, normalized : null}院士 | analysis : { original : TITLE, normalized : null}, | analysis : { original : O, normalized : null}他 | analysis : { original : O, normalized : null}于 | analysis : { original : O, normalized : null} | analysis : { original : DATE, normalized : -10-XX}10月 | analysis : { original : DATE, normalized : -10-XX}到 | analysis : { original : O, normalized : null}中国 | analysis : { original : COUNTRY, normalized : null}山东省 | analysis : { original : STATE_OR_PROVINCE, normalized : null}东营市 | analysis : { original : CITY, normalized : null}东营区 | analysis : { original : FACILITY, normalized : null}永乐 | analysis : { original : FACILITY, normalized : null}机场 | analysis : { original : FACILITY, normalized : null}附近 | analysis : { original : O, normalized : null}承包 | analysis : { original : O, normalized : null}了 | analysis : { original : O, normalized : null}一千 | analysis : { original : NUMBER, normalized : 1000}亩 | analysis : { original : O, normalized : null}盐 | analysis : { original : O, normalized : null}碱地 | analysis : { original : O, normalized : null}, | analysis : { original : O, normalized : null}开始 | analysis : { original : O, normalized : null}种植 | analysis : { original : O, normalized : null}棉花 | analysis : { original : O, normalized : null}, | analysis : { original : O, normalized : null}年产量 | analysis : { original : O, normalized : null}达到 | analysis : { original : O, normalized : null}一万 | analysis : { original : NUMBER, normalized : 10000}吨 | analysis : { original : O, normalized : null}, | analysis : { original : O, normalized : null}哈哈 | analysis : { original : O, normalized : null}, | analysis : { original : O, normalized : null}反正 | analysis : { original : O, normalized : null}棣琦 | analysis : { original : PERSON, normalized : null}说 | analysis : { original : O, normalized : null}的 | analysis : { original : O, normalized : null}是 | analysis : { original : O, normalized : null}假 | analysis : { original : O, normalized : null}的 | analysis : { original : O, normalized : null}, | analysis : { original : O, normalized : null}逗 | analysis : { original : O, normalized : null}你 | analysis : { original : O, normalized : null}玩儿 | analysis : { original : O, normalized : null}, | analysis : { original : O, normalized : null}明天 | analysis : { original : DATE, normalized : XXXX-XX-XX}下午 | analysis : { original : TIME, normalized : null}2点 | analysis : { original : TIME, normalized : null}来 | analysis : { original : O, normalized : null}我 | analysis : { original : O, normalized : null}家 | analysis : { original : O, normalized : null}吃饭 | analysis : { original : O, normalized : null}吧 | analysis : { original : O, normalized : null}。 | analysis : { original : O, normalized : null}棣琦 | analysis : { original : PERSON, normalized : null}是 | analysis : { original : O, normalized : null}山东 | analysis : { original : ORGANIZATION, normalized : null}大学 | analysis : { original : ORGANIZATION, normalized : null}毕业 | analysis : { original : O, normalized : null}的 | analysis : { original : O, normalized : null}, | analysis : { original : O, normalized : null}目前 | analysis : { original : DATE, normalized : null}在 | analysis : { original : O, normalized : null}百度 | analysis : { original : ORGANIZATION, normalized : null}做 | analysis : { original : O, normalized : null}java | analysis : { original : O, normalized : null}开发 | analysis : { original : O, normalized : null}, | analysis : { original : O, normalized : null}位置 | analysis : { original : O, normalized : null}是 | analysis : { original : O, normalized : null}东北 | analysis : { original : LOCATION, normalized : null}旺 | analysis : { original : O, normalized : null}东路 | analysis : { original : O, normalized : null}102 | analysis : { original : NUMBER, normalized : 102}号院 | analysis : { original : O, normalized : null}, | analysis : { original : O, normalized : null}手机号 | analysis : { original : O, normalized : null}143667788 | analysis : { original : NUMBER, normalized : 14366778890}90 | analysis : { original : NUMBER, normalized : 14366778890}The analysis lasts 819 seconds * 1000Process finished with exit code 0

我们可以看到，整个工程的启动耗时还是挺久的。分析过程也比较耗时，819毫秒。

并且结果也不够准确，跟我在其官网在线demo得到的结果还是有些差异的：

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