Confidence prediction in Stanford NER












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Confidence level to sequence prediction in Stanford NER Tagger. It's possible? Confidence for a given predicted sequence.










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    Confidence level to sequence prediction in Stanford NER Tagger. It's possible? Confidence for a given predicted sequence.










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      Confidence level to sequence prediction in Stanford NER Tagger. It's possible? Confidence for a given predicted sequence.










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      Confidence level to sequence prediction in Stanford NER Tagger. It's possible? Confidence for a given predicted sequence.







      nlp stanford-nlp named-entity-recognition






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      edited Nov 23 '18 at 3:56







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      asked Nov 23 '18 at 3:31









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          Here is some code that can print out the k (in the example 10) most likely sequences, and will print out the sequence probability.



          import edu.stanford.nlp.ie.AbstractSequenceClassifier;
          import edu.stanford.nlp.ie.crf.*;
          import edu.stanford.nlp.io.IOUtils;
          import edu.stanford.nlp.ling.CoreLabel;
          import edu.stanford.nlp.ling.CoreAnnotations;
          import edu.stanford.nlp.sequences.DocumentReaderAndWriter;
          import edu.stanford.nlp.util.Triple;

          import java.io.*;
          import java.util.List;


          public class GetCRFProbsDemo {

          public static void main(String args) throws ClassNotFoundException, IOException {
          String serializedClassifier = "edu/stanford/nlp/models/ner/english.all.3class.distsim.crf.ser.gz";
          AbstractSequenceClassifier<CoreLabel> classifier = CRFClassifier.getClassifier(serializedClassifier);
          System.out.println("---");
          System.out.println("Ten best entity labelings");
          DocumentReaderAndWriter<CoreLabel> readerAndWriter = classifier.makePlainTextReaderAndWriter();
          classifier.classifyAndWriteAnswersKBest(args[0], 10, readerAndWriter);
          }

          }





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            Here is some code that can print out the k (in the example 10) most likely sequences, and will print out the sequence probability.



            import edu.stanford.nlp.ie.AbstractSequenceClassifier;
            import edu.stanford.nlp.ie.crf.*;
            import edu.stanford.nlp.io.IOUtils;
            import edu.stanford.nlp.ling.CoreLabel;
            import edu.stanford.nlp.ling.CoreAnnotations;
            import edu.stanford.nlp.sequences.DocumentReaderAndWriter;
            import edu.stanford.nlp.util.Triple;

            import java.io.*;
            import java.util.List;


            public class GetCRFProbsDemo {

            public static void main(String args) throws ClassNotFoundException, IOException {
            String serializedClassifier = "edu/stanford/nlp/models/ner/english.all.3class.distsim.crf.ser.gz";
            AbstractSequenceClassifier<CoreLabel> classifier = CRFClassifier.getClassifier(serializedClassifier);
            System.out.println("---");
            System.out.println("Ten best entity labelings");
            DocumentReaderAndWriter<CoreLabel> readerAndWriter = classifier.makePlainTextReaderAndWriter();
            classifier.classifyAndWriteAnswersKBest(args[0], 10, readerAndWriter);
            }

            }





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              1














              Here is some code that can print out the k (in the example 10) most likely sequences, and will print out the sequence probability.



              import edu.stanford.nlp.ie.AbstractSequenceClassifier;
              import edu.stanford.nlp.ie.crf.*;
              import edu.stanford.nlp.io.IOUtils;
              import edu.stanford.nlp.ling.CoreLabel;
              import edu.stanford.nlp.ling.CoreAnnotations;
              import edu.stanford.nlp.sequences.DocumentReaderAndWriter;
              import edu.stanford.nlp.util.Triple;

              import java.io.*;
              import java.util.List;


              public class GetCRFProbsDemo {

              public static void main(String args) throws ClassNotFoundException, IOException {
              String serializedClassifier = "edu/stanford/nlp/models/ner/english.all.3class.distsim.crf.ser.gz";
              AbstractSequenceClassifier<CoreLabel> classifier = CRFClassifier.getClassifier(serializedClassifier);
              System.out.println("---");
              System.out.println("Ten best entity labelings");
              DocumentReaderAndWriter<CoreLabel> readerAndWriter = classifier.makePlainTextReaderAndWriter();
              classifier.classifyAndWriteAnswersKBest(args[0], 10, readerAndWriter);
              }

              }





              share|improve this answer


























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                1







                Here is some code that can print out the k (in the example 10) most likely sequences, and will print out the sequence probability.



                import edu.stanford.nlp.ie.AbstractSequenceClassifier;
                import edu.stanford.nlp.ie.crf.*;
                import edu.stanford.nlp.io.IOUtils;
                import edu.stanford.nlp.ling.CoreLabel;
                import edu.stanford.nlp.ling.CoreAnnotations;
                import edu.stanford.nlp.sequences.DocumentReaderAndWriter;
                import edu.stanford.nlp.util.Triple;

                import java.io.*;
                import java.util.List;


                public class GetCRFProbsDemo {

                public static void main(String args) throws ClassNotFoundException, IOException {
                String serializedClassifier = "edu/stanford/nlp/models/ner/english.all.3class.distsim.crf.ser.gz";
                AbstractSequenceClassifier<CoreLabel> classifier = CRFClassifier.getClassifier(serializedClassifier);
                System.out.println("---");
                System.out.println("Ten best entity labelings");
                DocumentReaderAndWriter<CoreLabel> readerAndWriter = classifier.makePlainTextReaderAndWriter();
                classifier.classifyAndWriteAnswersKBest(args[0], 10, readerAndWriter);
                }

                }





                share|improve this answer













                Here is some code that can print out the k (in the example 10) most likely sequences, and will print out the sequence probability.



                import edu.stanford.nlp.ie.AbstractSequenceClassifier;
                import edu.stanford.nlp.ie.crf.*;
                import edu.stanford.nlp.io.IOUtils;
                import edu.stanford.nlp.ling.CoreLabel;
                import edu.stanford.nlp.ling.CoreAnnotations;
                import edu.stanford.nlp.sequences.DocumentReaderAndWriter;
                import edu.stanford.nlp.util.Triple;

                import java.io.*;
                import java.util.List;


                public class GetCRFProbsDemo {

                public static void main(String args) throws ClassNotFoundException, IOException {
                String serializedClassifier = "edu/stanford/nlp/models/ner/english.all.3class.distsim.crf.ser.gz";
                AbstractSequenceClassifier<CoreLabel> classifier = CRFClassifier.getClassifier(serializedClassifier);
                System.out.println("---");
                System.out.println("Ten best entity labelings");
                DocumentReaderAndWriter<CoreLabel> readerAndWriter = classifier.makePlainTextReaderAndWriter();
                classifier.classifyAndWriteAnswersKBest(args[0], 10, readerAndWriter);
                }

                }






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                answered Nov 24 '18 at 5:34









                StanfordNLPHelpStanfordNLPHelp

                6,821159




                6,821159
































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