从NER获取全名

Question

通过阅读文档和使用应用程序接口，看起来CoreNLP会告诉我每个令牌的NER标记，但它不会帮助我从句子中提取全名。例如：

Input: John Wayne and Mary have coffee
CoreNLP Output: (John,PERSON) (Wayne,PERSON) (and,O) (Mary,PERSON) (have,O) (coffee,O)
Desired Result: list of PERSON ==> [John Wayne, Mary]

除非我遗漏了一些标志，否则我相信要做到这一点，我需要解析标记，并将标记为PERSON的连续标记粘合在一起。

有人能确认这确实是我需要做的吗？我主要想知道CoreNLP中是否有一些标志或实用程序可以为我做这样的事情。如果某人有一个实用程序(理想情况下是Java，因为我使用的是Java API)来做这件事并且想要分享:)

谢谢!

PS:有一个非常类似的问题，here，它似乎建议答案是“滚你自己的”，但它从未得到任何其他人的证实。

Answer 1

您可能正在寻找entity mentions而不是或以及NER标记。例如，使用Simple API

new Sentence("Jimi Hendrix was the greatest").nerTags()
[PERSON, PERSON, O, O, O]

new Sentence("Jimi Hendrix was the greatest").mentions()
[Jimi Hendrix]

上面的链接有一个使用老的StanfordCoreNLP管道的传统非简单API的示例。

Answer 2

此链接上的基本Java API示例中显示了这一点：

https://stanfordnlp.github.io/CoreNLP/api.html

以下是完整的Java API示例，其中有一节是关于实体的提及：

import edu.stanford.nlp.coref.data.CorefChain;
import edu.stanford.nlp.ling.*;
import edu.stanford.nlp.ie.util.*;
import edu.stanford.nlp.pipeline.*;
import edu.stanford.nlp.semgraph.*;
import edu.stanford.nlp.trees.*;
import java.util.*;


public class BasicPipelineExample {

  public static String text = "Joe Smith was born in California. " +
      "In 2017, he went to Paris, France in the summer. " +
      "His flight left at 3:00pm on July 10th, 2017. " +
      "After eating some escargot for the first time, Joe said, \"That was delicious!\" " +
      "He sent a postcard to his sister Jane Smith. " +
      "After hearing about Joe's trip, Jane decided she might go to France one day.";

  public static void main(String[] args) {
    // set up pipeline properties
    Properties props = new Properties();
    // set the list of annotators to run
    props.setProperty("annotators", "tokenize,ssplit,pos,lemma,ner,parse,depparse,coref,kbp,quote");
    // set a property for an annotator, in this case the coref annotator is being set to use the neural algorithm
    props.setProperty("coref.algorithm", "neural");
    // build pipeline
    StanfordCoreNLP pipeline = new StanfordCoreNLP(props);
    // create a document object
    CoreDocument document = new CoreDocument(text);
    // annnotate the document
    pipeline.annotate(document);
    // examples

    // 10th token of the document
    CoreLabel token = document.tokens().get(10);
    System.out.println("Example: token");
    System.out.println(token);
    System.out.println();

    // text of the first sentence
    String sentenceText = document.sentences().get(0).text();
    System.out.println("Example: sentence");
    System.out.println(sentenceText);
    System.out.println();

    // second sentence
    CoreSentence sentence = document.sentences().get(1);

    // list of the part-of-speech tags for the second sentence
    List<String> posTags = sentence.posTags();
    System.out.println("Example: pos tags");
    System.out.println(posTags);
    System.out.println();

    // list of the ner tags for the second sentence
    List<String> nerTags = sentence.nerTags();
    System.out.println("Example: ner tags");
    System.out.println(nerTags);
    System.out.println();

    // constituency parse for the second sentence
    Tree constituencyParse = sentence.constituencyParse();
    System.out.println("Example: constituency parse");
    System.out.println(constituencyParse);
    System.out.println();

    // dependency parse for the second sentence
    SemanticGraph dependencyParse = sentence.dependencyParse();
    System.out.println("Example: dependency parse");
    System.out.println(dependencyParse);
    System.out.println();

    // kbp relations found in fifth sentence
    List<RelationTriple> relations =
        document.sentences().get(4).relations();
    System.out.println("Example: relation");
    System.out.println(relations.get(0));
    System.out.println();

    // entity mentions in the second sentence
    List<CoreEntityMention> entityMentions = sentence.entityMentions();
    System.out.println("Example: entity mentions");
    System.out.println(entityMentions);
    System.out.println();

    // coreference between entity mentions
    CoreEntityMention originalEntityMention = document.sentences().get(3).entityMentions().get(1);
    System.out.println("Example: original entity mention");
    System.out.println(originalEntityMention);
    System.out.println("Example: canonical entity mention");
    System.out.println(originalEntityMention.canonicalEntityMention().get());
    System.out.println();

    // get document wide coref info
    Map<Integer, CorefChain> corefChains = document.corefChains();
    System.out.println("Example: coref chains for document");
    System.out.println(corefChains);
    System.out.println();

    // get quotes in document
    List<CoreQuote> quotes = document.quotes();
    CoreQuote quote = quotes.get(0);
    System.out.println("Example: quote");
    System.out.println(quote);
    System.out.println();

    // original speaker of quote
    // note that quote.speaker() returns an Optional
    System.out.println("Example: original speaker of quote");
    System.out.println(quote.speaker().get());
    System.out.println();

    // canonical speaker of quote
    System.out.println("Example: canonical speaker of quote");
    System.out.println(quote.canonicalSpeaker().get());
    System.out.println();

  }

}