nerlearner.py

from itertools import chain
import nltk
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.preprocessing import LabelBinarizer
import sklearn
import pycrfsuite


def word2features(sent, i):
    word = sent[i][0]
    postag = sent[i][1]
    features = [
        'bias',
        'word.lower=' + word.lower(),
        'word[-3:]=' + word[-3:],
        'word[-2:]=' + word[-2:],
        'word.isupper=%s' % word.isupper(),
        'word.istitle=%s' % word.istitle(),
        'word.isdigit=%s' % word.isdigit(),
        'postag=' + postag,
        'postag[:2]=' + postag[:2],
    ]
    if i > 0:
        word1 = sent[i - 1][0]
        postag1 = sent[i - 1][1]
        features.extend([
            '-1:word.lower=' + word1.lower(),
            '-1:word.istitle=%s' % word1.istitle(),
            '-1:word.isupper=%s' % word1.isupper(),
            '-1:postag=' + postag1,
            '-1:postag[:2]=' + postag1[:2],
        ])
    else:
        features.append('BOS')

    if i < len(sent) - 1:
        word1 = sent[i + 1][0]
        postag1 = sent[i + 1][1]
        features.extend([
            '+1:word.lower=' + word1.lower(),
            '+1:word.istitle=%s' % word1.istitle(),
            '+1:word.isupper=%s' % word1.isupper(),
            '+1:postag=' + postag1,
            '+1:postag[:2]=' + postag1[:2],
        ])
    else:
        features.append('EOS')

    return features


def sent2features(sent):
    return [word2features(sent, i) for i in range(len(sent))]


def sent2labels(sent):
    return [label for token, postag, label in sent]


def sent2tokens(sent):
    return [token for token, postag, label in sent]


print(sklearn.__version__)

print(nltk.corpus.conll2002.fileids())


train_sents = list(nltk.corpus.conll2002.iob_sents('esp.train'))
test_sents = list(nltk.corpus.conll2002.iob_sents('esp.testb'))

print(train_sents[0])

X_train = [sent2features(s) for s in train_sents]
y_train = [sent2labels(s) for s in train_sents]

X_test = [sent2features(s) for s in test_sents]
y_test = [sent2labels(s) for s in test_sents]


trainer = pycrfsuite.Trainer(verbose=False)

for xseq, yseq in zip(X_train, y_train):
    trainer.append(xseq, yseq)

trainer.set_params({
    'c1': 1.0,   # coefficient for L1 penalty
    'c2': 1e-3,  # coefficient for L2 penalty
    'max_iterations': 50,  # stop earlier

    # include transitions that are possible, but not observed
    'feature.possible_transitions': True
})

trainer.train('conll2002-esp.crfsuite')

print len(trainer.logparser.iterations), trainer.logparser.iterations[-1]