public static Set<String> analyzeThisFile(String path) {
String serializedClassifier = "classifiers/english.conll.4class.distsim.crf.ser.gz";
Set<String> annotationsNE = new HashSet<String>();
try {
BufferedReader br = new BufferedReader(new FileReader(path));
AbstractSequenceClassifier<CoreLabel> classifier =
CRFClassifier.getClassifier(serializedClassifier);
String line = "";
while ((line = br.readLine()) != null) {
List<Triple<String, Integer, Integer>> triples =
classifier.classifyToCharacterOffsets(line);
for (Triple<String, Integer, Integer> trip : triples) {
annotationsNE.add(line.substring(trip.second(), trip.third()));
}
}
System.out.println("Named Entity TROVATE");
for (String ne : annotationsNE) System.out.println(ne);
} catch (FileNotFoundException e) {
// TODO Auto-generated catch block
e.printStackTrace();
} catch (ClassCastException e) {
// TODO Auto-generated catch block
e.printStackTrace();
} catch (ClassNotFoundException e) {
// TODO Auto-generated catch block
e.printStackTrace();
} catch (IOException e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
return annotationsNE;
}
public static void main(String[] args) throws Exception {
// String serializedClassifier = "classifiers/english.all.3class.distsim.crf.ser.gz";
String serializedClassifier = "classifiers/english.muc.7class.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) {
// print entity/or non-entity - their nearby tokens
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 Washington DC on September 19. And Tomorrow.",
"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("---");
// ***sentence-by-sentence
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("---");
// ***print: entities + Classes + remaining text in the text
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
System.out.print("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("---");
}
}
