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Text_analysis_and_metadata_app / stanfordmodel /NERDemo.java
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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.util.List;
/** This is a demo of calling CRFClassifier programmatically.
* <p>
* Usage: {@code java -mx400m -cp "*" NERDemo [serializedClassifier [fileName]] }
* <p>
* If arguments aren't specified, they default to
* classifiers/english.all.3class.distsim.crf.ser.gz and some hardcoded sample text.
* If run with arguments, it shows some of the ways to get k-best labelings and
* probabilities out with CRFClassifier. If run without arguments, it shows some of
* the alternative output formats that you can get.
* <p>
* To use CRFClassifier from the command line:
* </p><blockquote>
* {@code java -mx400m edu.stanford.nlp.ie.crf.CRFClassifier -loadClassifier [classifier] -textFile [file] }
* </blockquote><p>
* Or if the file is already tokenized and one word per line, perhaps in
* a tab-separated value format with extra columns for part-of-speech tag,
* etc., use the version below (note the 's' instead of the 'x'):
* </p><blockquote>
* {@code java -mx400m edu.stanford.nlp.ie.crf.CRFClassifier -loadClassifier [classifier] -testFile [file] }
* </blockquote>
*
* @author Jenny Finkel
* @author Christopher Manning
*/
public class NERDemo {
public static void main(String[] args) throws Exception {
String serializedClassifier = "classifiers/english.all.3class.distsim.crf.ser.gz";
if (args.length > 0) {
serializedClassifier = args[0];
}
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("---");
}
}
}