/////////////////////////////////////////////////////
// RUNNING THE PARSER
////////////////////////////////////////////////////
public static void main(String[] args) throws FileNotFoundException, Exception {
System.setProperty("java.io.tmpdir", "./tmp/");
ParserOptions options = new ParserOptions(args);
System.out.println("Default temp directory:" + System.getProperty("java.io.tmpdir"));
System.out.println("Separate labeling: " + options.separateLab);
if (options.train) {
DependencyPipe pipe =
options.secondOrder ? new DependencyPipe2O(options) : new DependencyPipe(options);
int[] instanceLengths = pipe.createInstances(options.trainfile, options.trainforest);
pipe.closeAlphabets();
DependencyParser dp = new DependencyParser(pipe, options);
// pipe.printModelStats(null);
int numFeats = pipe.dataAlphabet.size();
int numTypes = pipe.typeAlphabet.size();
System.out.print("Num Feats: " + numFeats);
System.out.println(".\tNum Edge Labels: " + numTypes);
if (options
.stackedLevel0) // Augment training data with output predictions, for stacked learning
// (afm 03-03-08)
{
// Output data augmented with output predictions
System.out.println("Augmenting training data with output predictions...");
options.testfile = options.trainfile;
dp.augment(
instanceLengths, options.trainfile, options.trainforest, options.augmentNumParts);
// Now train the base classifier in the whole corpus, nothing being ignored
System.out.println("Training the base classifier in the whole corpus...");
}
// afm 03-06-08 --- To allow some instances to be ignored
int ignore[] = new int[instanceLengths.length];
for (int i = 0; i < instanceLengths.length; i++) ignore[i] = 0;
dp.params = new Parameters(pipe.dataAlphabet.size());
dp.train(instanceLengths, ignore, options.trainfile, options.trainforest);
System.out.print("Saving model...");
dp.saveModel(options.modelName);
System.out.print("done.");
}
if (options.test) {
DependencyPipe pipe =
options.secondOrder ? new DependencyPipe2O(options) : new DependencyPipe(options);
DependencyParser dp = new DependencyParser(pipe, options);
System.out.print("\tLoading model...");
dp.loadModel(options.modelName);
System.out.println("done.");
pipe.printModelStats(dp.params);
pipe.closeAlphabets();
dp.outputParses(null);
}
System.out.println();
if (options.eval) {
System.out.println("\nEVALUATION PERFORMANCE:");
DependencyEvaluator.evaluate(options.goldfile, options.outfile, options.format);
}
}
// ///////////////////////////////////////////////////
// RUNNING THE PARSER
// //////////////////////////////////////////////////
public static void main(String[] args) throws FileNotFoundException, Exception {
ParserOptions options = new ParserOptions(args);
if (options.train) {
DependencyPipe pipe =
options.secondOrder ? new DependencyPipe2O(options) : new DependencyPipe(options);
int[] instanceLengths = pipe.createInstances(options.trainfile, options.trainforest);
pipe.closeAlphabets();
DependencyParser dp = new DependencyParser(pipe, options);
int numFeats = pipe.dataAlphabet.size();
int numTypes = pipe.typeAlphabet.size();
System.out.print("Num Feats: " + numFeats);
System.out.println(".\tNum Edge Labels: " + numTypes);
dp.train(instanceLengths, options.trainfile, options.trainforest);
System.out.print("Saving model...");
dp.saveModel(options.modelName);
System.out.print("done.");
}
if (options.test) {
DependencyPipe pipe =
options.secondOrder ? new DependencyPipe2O(options) : new DependencyPipe(options);
scoreWriter = new BufferedWriter(new FileWriter(options.outfile + ".mstscores"));
DependencyParser dp = new DependencyParser(pipe, options);
System.out.print("\tLoading model...");
dp.loadModel(options.modelName);
System.out.println("done.");
pipe.closeAlphabets();
dp.outputParses();
scoreWriter.close();
}
System.out.println();
if (options.eval) {
System.out.println("\nEVALUATION PERFORMANCE:");
DependencyEvaluator.evaluate(
options.goldfile, options.outfile, options.format, (options.confidenceEstimator != null));
}
if (options.rankEdgesByConfidence) {
System.out.println("\nRank edges by confidence:");
EdgeRankerByConfidence edgeRanker = new EdgeRankerByConfidence();
edgeRanker.rankEdgesByConfidence(options.goldfile, options.outfile, options.format);
}
}
