Question

我是NLP领域的新手，我有兴趣检测位置/名称/角色以及他们的姓名，电子邮件，电话号码等。我尝试使用stanford NLP来检测文本中的姓名。电子邮件和电话号码解析似乎非常简单。但是，我无法从给定的文本中检测到该指定。

例如，以下是一些文本示例

1）医疗主管，博士。 A.B.艾哈迈德例1 @ example.com
名称：博士。 A.B.艾哈迈德，电子邮件：example1@example.com

2）Sub-Dean Academics S. Antony教授example2@example.com
名称：教授。 S. Antony，电子邮件：example2@example.com

3）Sub-Dean Academics＆amp; PG-Cell＆amp;外科杂志。纪律居民Trg。 PROGRAME，先生。桑迪普
姓名：Sandeep先生，电子邮件：无

4）网络总监Robert Adams，example3 @ example.com，9900131213
姓名：Robert Adams，电子邮件：example3 @ example.com，电话：9900131213

我对任何正则表达式匹配算法不感兴趣，因为文本的性质是非确定性的。我感兴趣的是如何从文本中提取上述设计。任何解决方案甚至超越斯坦福NLP，如使用nltk，lingpipe等都很好。如果我正在使用stanford NLP，我如何使用不同的实体类型（如“POSITION”或“DESIGNATION”）构建相同的训练模型，以及如何将此模型与其他模型一起包含（我在服务器中运行stanford NLP）模式）。

Answer 1

尝试使用以下文件（designation.rules.txt）

ENV.defaultStringPatternFlags = 2

ner = { type: "CLASS", value: "edu.stanford.nlp.ling.CoreAnnotations$NamedEntityTagAnnotation" }
tokens = { type: "CLASS", value: "edu.stanford.nlp.ling.CoreAnnotations$TokensAnnotation" }

$Designation = (
  /CFO/|
  /Director/| 
  /CEO/|
  /Chief/| 
  /Executive/| 
  /Officer/|
  /Vice/| 
  /President/|
  /Senior/|
  /Financial/
)

ENV.defaults["ruleType"] = "tokens"
ENV.defaults["stage"] = 1
{
  pattern: ( $Designation ), 
  action: ( Annotate($0, ner, "DESIGNATION")) 
}

ENV.defaults["stage"] = 2
{
  ruleType: "tokens",
  pattern: ( ( [ { ner:PERSON } ]) /has/ ([ { ner:DESIGNATION } ]+) ),
  result: Format("hasDesignation(%s,%s)",$1.word, Join(" ",$2.word))
}

使用以下Java文件生成

package org.itcookies.nlpdemo;

import java.io.IOException;
import java.io.PrintWriter;
import java.util.List;
import java.util.Properties;

import edu.stanford.nlp.io.IOUtils;
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.util.CoreMap;

/**
 * Demo illustrating how to use TokensRegexAnnotator
 */
public class TokensRegexAnnotatorDemo {

  public static void main(String[] args) throws IOException {
    PrintWriter out;

    String rules;
    if (args.length > 0) {
      rules = args[0];
    } else {
      rules = "org/itcookies/nlp/rules/designation.rules.txt";
    }
    if (args.length > 2) {
      out = new PrintWriter(args[2]);
    } else {
      out = new PrintWriter(System.out);
    }

    Properties properties = new Properties();
    properties.setProperty("annotators", "tokenize,ssplit,pos,lemma,ner,tokensregexdemo");
    properties.setProperty("customAnnotatorClass.tokensregexdemo", "edu.stanford.nlp.pipeline.TokensRegexAnnotator");
    properties.setProperty("tokensregexdemo.rules", rules);
    StanfordCoreNLP pipeline = new StanfordCoreNLP(properties);
    Annotation annotation;
    if (args.length > 1) {
      annotation = new Annotation(IOUtils.slurpFileNoExceptions(args[1]));
    } else {
      annotation = new Annotation("John is CEO of ITCookies");
    }

    pipeline.annotate(annotation);

    // An Annotation is a Map and you can get and use the various analyses individually.
    out.println();
    // The toString() method on an Annotation just prints the text of the Annotation
    // But you can see what is in it with other methods like toShorterString()
    out.println("The top level annotation");
    out.println(annotation.toShorterString());
    List<CoreMap> sentences = annotation.get(CoreAnnotations.SentencesAnnotation.class);

    for (CoreMap sentence : sentences) {
      // NOTE: Depending on what tokensregex rules are specified, there are other annotations
      //       that are of interest other than just the tokens and what we print out here
      for (CoreLabel token:sentence.get(CoreAnnotations.TokensAnnotation.class)) {
        // Print out words, lemma, ne, and normalized ne
        String word = token.get(CoreAnnotations.TextAnnotation.class);
        String lemma = token.get(CoreAnnotations.LemmaAnnotation.class);
        String pos = token.get(CoreAnnotations.PartOfSpeechAnnotation.class);
        String ne = token.get(CoreAnnotations.NamedEntityTagAnnotation.class);
        String normalized = token.get(CoreAnnotations.NormalizedNamedEntityTagAnnotation.class);
        if(ne.equals("DESIGNATION"))
            out.println("token: " + "word="+word + ", lemma="+lemma + ", pos=" + pos + ", ne=" + ne + ", normalized=" + normalized);
      }
    }
    out.flush();
  }

}

以下是输出

The top level annotation
[Text=John is CEO of ITCookies Tokens=[John-1, is-2, CEO-3, of-4, ITCookies-5] Sentences=[John is CEO of ITCookies]]
token: word=CEO, lemma=CEO, pos=NNP, ne=DESIGNATION, normalized=null

使用ner / nlp从文本中检测员工姓名

1 个答案: