def main():
parser = argparse.ArgumentParser(description='clwsd')
parser.add_argument('--sourceword', type=str, required=True)
parser.add_argument('--targetlang', type=str, required=True)
parser.add_argument('--taggerhome', type=str, required=True)
args = parser.parse_args()
all_target_languages = "de es fr it nl".split()
assert args.targetlang in all_target_languages
target = args.targetlang
sourceword = args.sourceword
stanford.taggerhome = args.taggerhome
gold_answers = read_gold.get_gold_answers(sourceword, target)
instances = get_training_data(sourceword, target)
print("... training ...")
nltk.classify.megam.config_megam(bin='/usr/local/bin/megam')
classifier = MaxentClassifier.train(instances, trace=0, algorithm='megam')
print("LABELS", classifier.labels())
## with open("../eval/{0}.output".format(sourceword), "w") as outfile:
fn = "../trialdata/alltrials/{0}.data".format(sourceword)
problems = extract_wsd_problems(fn)
for problem in problems:
featureset = features.extract(problem)
answer = classifier.classify(featureset)
print(output_one_best(problem, target, answer))
label = gold_answers[problem.instance_id]
print("CORRECT" if label == answer else "WRONG")
print("distribution was...")
dist = classifier.prob_classify(featureset)
for key in dist.samples():
print(" ", key, dist.prob(key))
def main():
parser = argparse.ArgumentParser(description='clwsd')
parser.add_argument('--sourceword', type=str, nargs=1, required=True)
parser.add_argument('--targetlang', type=str, nargs=1, required=True)
parser.add_argument('--classifier', type=str, nargs=1, required=False)
args = parser.parse_args()
all_target_languages = "de es fr it nl".split()
assert args.targetlang[0] in all_target_languages
target = args.targetlang[0]
sourceword = args.sourceword[0]
nltk.classify.megam.config_megam(bin='/usr/local/bin/megam')
classifier = get_maxent_classifier(sourceword, target)
fn = "../trialdata/alltrials/{0}.data".format(sourceword)
## XXX(alexr): fix later.
stanford.taggerhome = "/home/alex/software/stanford-postagger-2012-11-11"
problems = extract_wsd_problems(fn)
gold_answers = read_gold.get_gold_answers(sourceword, target)
for problem in problems:
featureset = features.extract(problem)
answer = classifier.classify(featureset)
print(problem.tokenized)
print(answer)
label = gold_answers[problem.instance_id]
print("CORRECT" if label == answer else "WRONG", end=" ")
print("should be:", label)
def get_training_data(sourceword, target):
"""Return a list of (featureset, label) for training."""
out = []
## map from id to labels
gold_answers = read_gold.get_gold_answers(sourceword, target)
problems = get_training_problems(sourceword)
## now collate them.
for problem in problems:
theid = problem.instance_id
featureset = features.extract(problem)
label = gold_answers[theid]
out.append((featureset, label))
return out