NLP工具——stanford coreNLP NER模块学习

环境

1. Mac
2. java1.8
3. Maven

一、调用

1. pom依赖

   1. 
      	edu.stanford.nlp
      	stanford-corenlp
      	3.9.1
      
      
      	edu.stanford.nlp
      	stanford-corenlp
      	3.9.1
      	models
      
      
      	edu.stanford.nlp
      	stanford-corenlp
      	3.9.1
      	models-chinese
      

       

2. 调用

   1. 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;
      
      public class TestNLP {
          public static void main(String[] args) throws  Exception {
              TestNLP nlp=new TestNLP();
              nlp.test();
          }
      
          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2009年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 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)) {
                      //                // 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 corefChains = document.get(CorefCoreAnnotations.CorefChainAnnotation.class);
              if (corefChains == null) {
                  return;
              }
              for (Map.Entry 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 0
                      List 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() + ")");
                  }
              }
          }
      }

       

二、参考：

1. 使用Standford coreNLP进行中文命名实体识别
2. 下载：https://nlp.stanford.edu/software/segmenter.shtml#Download
3. http://www.voidcn.com/article/p-wermgvyt-bpu.html
4. http://www.voidcn.com/article/p-obzqxwhs-bmq.html
5. http://www.limisky.com/108.html
6. http://www.voidcn.com/article/p-slejtrfb-bpv.htm