Example usage for edu.stanford.nlp.ie AbstractSequenceClassifier classifyToString

Introduction

In this page you can find the example usage for edu.stanford.nlp.ie AbstractSequenceClassifier classifyToString.

Prototype

public String classifyToString(String sentences) 

Source Link

Document

Classify the contents of a String to a tagged word/class String.

Usage

From source file:fire.NERDemo.java

public static void main(String[] args) throws Exception {

    String serializedClassifier = "C:\\Users\\DIPANAKR\\Desktop\\Satanu\\fire\\stanford-ner-2015-04-20\\stanford-ner-2015-04-20\\classifiers\\english.all.3class.distsim.crf.ser.gz";

    if (args.length > 0) {
        serializedClassifier = args[0];/*from  ww w.  jav  a2  s . c  o m*/
    }

    AbstractSequenceClassifier<CoreLabel> classifier = CRFClassifier.getClassifier(serializedClassifier);

    /* For either a file to annotate or for the hardcoded text example, this
       demo file shows several ways to process the input, for teaching purposes.
    */

    if (args.length > 1) {

        /* For the file, it shows (1) how to run NER on a String, (2) how
           to get the entities in the String with character offsets, and
           (3) how to run NER on a whole file (without loading it into a String).
        */

        String fileContents = IOUtils.slurpFile(args[1]);
        List<List<CoreLabel>> out = classifier.classify(fileContents);
        for (List<CoreLabel> sentence : out) {
            for (CoreLabel word : sentence) {
                System.out.print(word.word() + '/' + word.get(CoreAnnotations.AnswerAnnotation.class) + ' ');
            }
            System.out.println();
        }

        System.out.println("---");
        out = classifier.classifyFile(args[1]);
        for (List<CoreLabel> sentence : out) {
            for (CoreLabel word : sentence) {
                System.out.print(word.word() + '/' + word.get(CoreAnnotations.AnswerAnnotation.class) + ' ');
            }
            System.out.println();
        }

        System.out.println("---");
        List<Triple<String, Integer, Integer>> list = classifier.classifyToCharacterOffsets(fileContents);
        for (Triple<String, Integer, Integer> item : list) {
            System.out.println(item.first() + ": " + fileContents.substring(item.second(), item.third()));
        }
        System.out.println("---");
        System.out.println("Ten best entity labelings");
        DocumentReaderAndWriter<CoreLabel> readerAndWriter = classifier.makePlainTextReaderAndWriter();
        classifier.classifyAndWriteAnswersKBest(args[1], 10, readerAndWriter);

        System.out.println("---");
        System.out.println("Per-token marginalized probabilities");
        classifier.printProbs(args[1], readerAndWriter);

        // -- This code prints out the first order (token pair) clique probabilities.
        // -- But that output is a bit overwhelming, so we leave it commented out by default.
        // System.out.println("---");
        // System.out.println("First Order Clique Probabilities");
        // ((CRFClassifier) classifier).printFirstOrderProbs(args[1], readerAndWriter);

    } else {

        /* For the hard-coded String, it shows how to run it on a single
           sentence, and how to do this and produce several formats, including
           slash tags and an inline XML output format. It also shows the full
           contents of the {@code CoreLabel}s that are constructed by the
           classifier. And it shows getting out the probabilities of different
           assignments and an n-best list of classifications with probabilities.
        */

        String[] example = { "Good afternoon Rajat Raina, how are you today?",
                "I go to school at Stanford University, which is located in California." };
        for (String str : example) {
            System.out.println(classifier.classifyToString(str));
        }
        System.out.println("---");

        for (String str : example) {
            // This one puts in spaces and newlines between tokens, so just print not println.
            System.out.print(classifier.classifyToString(str, "slashTags", false));
        }
        System.out.println("---");

        for (String str : example) {
            // This one is best for dealing with the output as a TSV (tab-separated column) file.
            // The first column gives entities, the second their classes, and the third the remaining text in a document
            System.out.print(classifier.classifyToString(str, "tabbedEntities", false));
        }
        System.out.println("---");

        for (String str : example) {
            System.out.println(classifier.classifyWithInlineXML(str));
        }
        System.out.println("---");

        for (String str : example) {
            System.out.println(classifier.classifyToString(str, "xml", true));
        }
        System.out.println("---");

        for (String str : example) {
            System.out.print(classifier.classifyToString(str, "tsv", false));
        }
        System.out.println("---");

        // This gets out entities with character offsets
        int j = 0;
        for (String str : example) {
            j++;
            List<Triple<String, Integer, Integer>> triples = classifier.classifyToCharacterOffsets(str);
            for (Triple<String, Integer, Integer> trip : triples) {
                System.out.printf("%s over character offsets [%d, %d) in sentence %d.%n", trip.first(),
                        trip.second(), trip.third, j);
            }
        }
        System.out.println("---");

        // This prints out all the details of what is stored for each token
        int i = 0;
        for (String str : example) {
            for (List<CoreLabel> lcl : classifier.classify(str)) {
                for (CoreLabel cl : lcl) {
                    System.out.print(i++ + ": ");
                    System.out.println(cl.toShorterString());
                }
            }
        }

        System.out.println("---");

    }
}

---

From source file:fire.trainingFile.java

public void NEdetection() throws Exception {

    String serializedClassifier = "C:\\Users\\DIPANAKR\\Desktop\\Satanu\\fire\\stanford-ner-2015-04-20\\stanford-ner-2015-04-20\\classifiers\\english.all.3class.distsim.crf.ser.gz";
    AbstractSequenceClassifier<CoreLabel> classifier = CRFClassifier.getClassifier(serializedClassifier);

    String line = "";
    for (int i = 0; i < n; i++) {
        line = line + words[i] + " ";
    }/*from  w  ww.j a va  2 s  .c om*/

    String str = classifier.classifyToString(line);
    String wrd[] = str.split(" ");

    for (int i = 0; i < wrd.length; i++) {

        if (wrd[i].endsWith("/O")) {
            isNEStan[i] = 0;
        } else if (!wrd[i].endsWith("/O")) {
            System.out.println(wrd[i]);
            isNEStan[i] = 1;
        }
    }

}

---

From source file:graphene.augment.snlp.NERDemo.java

License:Apache License

public static void main(String[] args) throws Exception {

    String serializedClassifier = "src/main/resources/edu/stanford/nlp/models/ner/english.all.3class.caseless.distsim.crf.ser.gz";

    if (args.length > 0) {
        serializedClassifier = args[0];/* w w  w .  j  av a2s  .co  m*/
    }

    AbstractSequenceClassifier<CoreLabel> classifier = CRFClassifier.getClassifier(serializedClassifier);

    /*
     * For either a file to annotate or for the hardcoded text example, this
     * demo file shows two ways to process the output, for teaching
     * purposes. For the file, it shows both how to run NER on a String and
     * how to run it on a whole file. For the hard-coded String, it shows
     * how to run it on a single sentence, and how to do this and produce an
     * inline XML output format.
     */
    if (args.length > 1) {
        String fileContents = IOUtils.slurpFile(args[1]);
        List<List<CoreLabel>> out = classifier.classify(fileContents);
        for (List<CoreLabel> sentence : out) {
            for (CoreLabel word : sentence) {
                System.out.print(word.word() + '/' + word.get(CoreAnnotations.AnswerAnnotation.class) + ' ');
            }
            System.out.println();
        }
        System.out.println("---");
        out = classifier.classifyFile(args[1]);
        for (List<CoreLabel> sentence : out) {
            for (CoreLabel word : sentence) {
                System.out.print(word.word() + '/' + word.get(CoreAnnotations.AnswerAnnotation.class) + ' ');
            }
            System.out.println();
        }

    } else {
        String[] example = { "Good afternoon Rajat Raina, how are you today?",
                "I go to school at Stanford University, which is located in California." };
        for (String str : example) {
            System.out.println(classifier.classifyToString(str));
        }
        System.out.println("---");

        for (String str : example) {
            // This one puts in spaces and newlines between tokens, so just
            // print not println.
            System.out.print(classifier.classifyToString(str, "slashTags", false));
        }
        System.out.println("---");

        for (String str : example) {
            System.out.println(classifier.classifyWithInlineXML(str));
        }
        System.out.println("---");

        for (String str : example) {
            System.out.println(classifier.classifyToString(str, "xml", true));
        }
        System.out.println("---");

        int i = 0;
        for (String str : example) {
            for (List<CoreLabel> lcl : classifier.classify(str)) {
                for (CoreLabel cl : lcl) {
                    System.out.print(i++ + ": ");
                    System.out.println(cl.toShorterString());
                }
            }
        }
    }
}

---

From source file:nlidb.NLIDB.java

public static void main(String[] args) throws Exception {

    //String serializedClassifier = "classifiers/english.all.3class.distsim.crf.ser.gz";
    //String serializedClassifier = "nlidbtraining.ser.gz";
    String serializedClassifier = "classifiers/ner-phd-test.ser.gz";

    if (args.length > 0) {
        serializedClassifier = args[0];/*from w w  w. ja  v  a2s  . c om*/
    }

    AbstractSequenceClassifier<CoreLabel> classifier = CRFClassifier.getClassifier(serializedClassifier);

    /* For either a file to annotate or for the hardcoded text example,
       this demo file shows two ways to process the output, for teaching
       purposes.  For the file, it shows both how to run NER on a String
       and how to run it on a whole file.  For the hard-coded String,
       it shows how to run it on a single sentence, and how to do this
       and produce an inline XML output format.
    */
    if (args.length > 1) {
        String fileContents = IOUtils.slurpFile(args[1]);
        List<List<CoreLabel>> out = classifier.classify(fileContents);
        for (List<CoreLabel> sentence : out) {
            for (CoreLabel word : sentence) {
                System.out.print(word.word() + '/' + word.get(CoreAnnotations.AnswerAnnotation.class) + ' ');
            }
            System.out.println();
        }
        System.out.println("---");
        out = classifier.classifyFile(args[1]);
        for (List<CoreLabel> sentence : out) {
            for (CoreLabel word : sentence) {
                System.out.print(word.word() + '/' + word.get(CoreAnnotations.AnswerAnnotation.class) + ' ');
            }
            System.out.println();
        }

    } else {
        //String[] example = {"Good afternoon Rajat Raina, how are you today?",
        //                    "I go to school at Stanford University, which is located in California." };

        String[] example = { "which customer has the postcode CM13" };
        for (String str : example) {
            System.out.println(classifier.classifyToString(str));
        }
        System.out.println("---");

        for (String str : example) {
            // This one puts in spaces and newlines between tokens, so just print not println.
            System.out.print(classifier.classifyToString(str, "slashTags", false));
        }
        System.out.println("---");

        for (String str : example) {
            System.out.println(classifier.classifyWithInlineXML(str));
        }
        System.out.println("---");

        for (String str : example) {
            System.out.println(classifier.classifyToString(str, "xml", true));
        }
        System.out.println("---");

        int i = 0;
        for (String str : example) {
            for (List<CoreLabel> lcl : classifier.classify(str)) {
                for (CoreLabel cl : lcl) {
                    System.out.print(i++ + ": ");
                    System.out.println(cl.toShorterString());
                }
            }
        }
    }
}

---