def feature_extractor(X):
# Apply attribute templates to obtain features.
crfutils.apply_templates(X, templates)
# Add the is_punct() feature.
map(lambda x: x['F'].append("punct=" + is_punct(x['c'])), X)
# Add the char_class() feature.
map(lambda x: x['F'].append("class=" + char_class(x['c'])), X)
def feature_extractor(X):
# Apply attribute templates to obtain features (in fact, attributes)
crfutils.apply_templates(X, templates)
if X:
# Append BOS and EOS features manually
X[0]['F'].append('__BOS__') # BOS feature
X[-1]['F'].append('__EOS__') # EOS feature
def feature_extractor(X):
for x in X:
observation(x)
crfutils.apply_templates(X, templates)
if X:
X[0]['F'].append('__BOS__')
X[-1]['F'].append('__EOS__')
def feature_extractor(X):
# Apply attribute templates to obtain features.
crfutils.apply_templates(X, templates)
# Add the is_punct() feature.
map(lambda x: x['F'].append("punct=" + is_punct(x['c'])), X)
# Add the char_class() feature.
map(lambda x: x['F'].append("class=" + char_class(x['c'])), X)
def feature_extractor(X):
# Apply attribute templates to obtain features (in fact, attributes)
crfutils.apply_templates(X, templates)
if X:
# Append BOS and EOS features manually
X[0]['F'].append('__BOS__') # BOS feature
X[-1]['F'].append('__EOS__') # EOS feature
def FeatureExtractor(X):
"""apply attribute templates to obtain features (in fact, attributes)"""
crfutils.apply_templates(X, templates)
Featurizer(X)
if X:
X[0]['F'].append('__BOS__') # BOS feature
X[-1]['F'].append('__EOS__') # EOS feature
def FeatureExtractor(X):
"""apply attribute templates to obtain features (in fact, attributes)"""
#print 'FeatureExtractor called with X ', X
crfutils.apply_templates(X, templates)
Featurizer(X)
#print X
if X:
#print 'in if X'
X[0]['F'].append('__BOS__') # BOS feature
X[-1]['F'].append('__EOS__') # EOS feature
def FeatureExtractor(X):
"""apply attribute templates to obtain features (in fact, attributes)"""
#print 'FeatureExtractor called with X ', X
crfutils.apply_templates(X, templates)
Featurizer(X)
#print X
if X:
#print 'in if X'
X[0]['F'].append('__BOS__') # BOS feature
X[-1]['F'].append('__EOS__') # EOS feature
def feature_extractor(X):
# Append observations.
for x in X:
observation(x)
# Apply the feature templates.
crfutils.apply_templates(X, templates)
if X:
# Append BOS and EOS features manually
X[0]['F'].append('__BOS__') # BOS feature
X[-1]['F'].append('__EOS__') # EOS feature
def feature_extractor(X):
# Append observations.
for x in X:
observation(x)
# Apply the feature templates.
crfutils.apply_templates(X, templates)
# Append disjunctive features.
for t in range(len(X)):
disjunctive(X, t, 'w', -4, -1)
disjunctive(X, t, 'w', 1, 4)
# Append BOS and EOS features.
if X:
X[0]['F'].append('__BOS__')
X[-1]['F'].append('__EOS__')
def feature_extractor(X):
# Append observations.
for x in X:
observation(x)
# Apply the feature templates.
crfutils.apply_templates(X, templates)
# Append disjunctive features.
for t in range(len(X)):
disjunctive(X, t, 'w', -4, -1)
disjunctive(X, t, 'w', 1, 4)
# Append BOS and EOS features.
if X:
X[0]['F'].append('__BOS__')
X[-1]['F'].append('__EOS__')
def feature_extractor(self, X):
# Append observations.
for x in X:
self.observation(x)
gen_regex_observation(X)
gen_gazetteer_observation(X, gazetteer=self.gazetteer)
# Apply the feature templates.
crfutils.apply_templates(X, self.templates)
# Append disjunctive features.
for t in range(len(X)):
disjunctive(X, t, 'w', -4, -1)
disjunctive(X, t, 'w', 1, 4)
if self.use_fregex:
regexp_features(X, t)
# Append BOS and EOS features.
if X:
X[0]['F'].append('__BOS__')
X[-1]['F'].append('__EOS__')
def feature_extractor(X):
# Apply templates to obtain features.
crfutils.apply_templates(X, templates)
# Add the is_punct() feature.
map(lambda x: x['F'].append("punct=" + is_punct(x['c'])), X)
def feature_extractor(sentence):
crfutils.apply_templates(sentence, attribute_templates)
def feature_extractor(X):
# Apply templates to obtain features.
crfutils.apply_templates(X, templates)
# Add the is_punct() feature.
map(lambda x: x['F'].append("punct=" + is_punct(x['c'])), X)
def extract_features(self, seq, content_type):
ret = []
if content_type == 'Blog':
use_brown_clusters = self.params.get_boolean('blog.use_brown_clusters')
use_traditional_features = self.params.get_boolean('blog.use_traditional_features')
use_idf_word_features = self.params.get_boolean('blog.use_idf_word_features')
use_embeddings = self.params.get_boolean('blog.use_embeddings')
use_lowercase_embeddings = self.params.get_boolean('blog.use_lowercase_embeddings')
use_postag = self.params.get_boolean('blog.use_postag')
elif content_type == 'SocialMediaPosting':
use_brown_clusters = self.params.get_boolean('tweet.use_brown_clusters')
use_traditional_features = self.params.get_boolean('tweet.use_traditional_features')
use_idf_word_features = self.params.get_boolean('tweet.use_idf_word_features')
use_embeddings = self.params.get_boolean('tweet.use_embeddings')
use_lowercase_embeddings = self.params.get_boolean('tweet.use_lowercase_embeddings')
use_postag = self.params.get_boolean('tweet.use_postag')
elif content_type == 'NewsArticle':
use_brown_clusters = self.params.get_boolean('news.use_brown_clusters')
use_traditional_features = self.params.get_boolean('news.use_traditional_features')
use_idf_word_features = self.params.get_boolean('news.use_idf_word_features')
use_embeddings = self.params.get_boolean('news.use_embeddings')
use_lowercase_embeddings = self.params.get_boolean('news.use_lowercase_embeddings')
use_postag = self.params.get_boolean('news.use_postag')
elif content_type == 'Post':
use_brown_clusters = self.params.get_boolean('dw.use_brown_clusters')
use_traditional_features = self.params.get_boolean('dw.use_traditional_features')
use_idf_word_features = self.params.get_boolean('dw.use_idf_word_features')
use_embeddings = self.params.get_boolean('dw.use_embeddings')
use_lowercase_embeddings = self.params.get_boolean('dw.use_lowercase_embeddings')
use_postag = self.params.get_boolean('dw.use_postag')
if use_traditional_features:
ner_seq = [{'w': x[0], 'F':[]} for x in seq]
for x in ner_seq:
ner.observation(x)
crfutils.apply_templates(ner_seq, self.templates)
# ner_seq is the same len as seq. Also, ner_seq[F] is a list of features
for i in range(2, len(seq)-2):
fs = [] # list of features for this word
if use_traditional_features:
fs.extend(ner_seq[i]['F'])
if use_idf_word_features:
fs.extend(self.get_idf_word_features(seq[i][0], i-2)) # subtract two to get "real" index
# word features
#fs.append('U00=%s' % seq[i-2][0]) # word left-2
#fs.append('U01=%s' % seq[i-1][0]) # word left-1
#fs.append('U02=%s' % seq[i][0]) # current word (w)
#fs.append('U03=%s' % seq[i+1][0]) # word right+1
#fs.append('U04=%s' % seq[i+2][0]) # word right+2
#fs.append('U05=%s/%s' % (seq[i-1][0], seq[i][0])) # w_left-2 / w
#fs.append('U06=%s/%s' % (seq[i][0], seq[i+1][0])) # w / w_right+1
# lowercase features
#fs.append('U00lc=%s' % seq[i-2][0].lower()) # word left-2
#fs.append('U01lc=%s' % seq[i-1][0].lower()) # word left-1
#fs.append('U02lc=%s' % seq[i][0].lower()) # current word (w)
#fs.append('U03lc=%s' % seq[i+1][0].lower()) # word right+1
#fs.append('U04lc=%s' % seq[i+2][0].lower()) # word right+2
#fs.append('U05lc=%s/%s' % (seq[i-1][0].lower(), seq[i][0].lower())) # w_left-2 / w
#fs.append('U06lc=%s/%s' % (seq[i][0].lower(), seq[i+1][0].lower())) # w / w_right+1
# This will only be non-empty if word lists are specified
fs.extend(self.get_word_list_features(seq[i][0]))
if use_brown_clusters:
# Size is the number of bits in the brown prefix returned
for size in [8, 12, 16, 20]:
# Get results for five-word window
for index in [-2, -1, 0, 1, 2]:
bc = self.get_brown_prefix(seq[i+index][0], size)
if bc:
fs.append("brown.%d.%d=%s" % (size, index, bc))
# embeddings
# TODO : try changing the %g to %f
if use_embeddings:
for j, embedding in enumerate(self.word_to_embedding[content_type]):
# U00e0-0=1:float U00e0-1=1:float U00e1-399=1:float , word_left-2 embeddings
# U01e0-0=1:float ... , word_left-1 embeddings
# U02e... , word w embeddings
# U03e... , word_right+1 embeddings
# U04e... , word_right+2 embeddings
for name, pos in zip(["U00", "U01", "U02", "U03", "U04"], [i-2,i-1,i,i+1,i+2]):
w = seq[pos][0] # word in that position/index
if w not in embedding: w = "*UNKNOWN*" # default all OOV words to the UNKNOWN embeddings
for d in range(len(embedding[w])):
fs.append("%se%d-%d=1:%g" % (name, j, d, embedding[w][d]))
if use_lowercase_embeddings:
for j, embedding in enumerate(self.word_to_embedding[content_type]):
for name, pos in zip(["U00", "U01", "U02", "U03", "U04"], [i-2,i-1,i,i+1,i+2]):
w = seq[pos][0] # word in that position/index
wlc = w.lower()
if wlc not in embedding: wlc = "*UNKNOWN*" # default all OOV words to the UNKNOWN embeddings
for d in range(len(embedding[wlc])):
fs.append("%slce%d-%d=1:%g" % (name, j, d, embedding[wlc][d]))
if use_postag:
fs.append('U10=%s' % seq[i-2][1])
fs.append('U11=%s' % seq[i-1][1])
fs.append('U12=%s' % seq[i][1])
fs.append('U13=%s' % seq[i+1][1])
fs.append('U14=%s' % seq[i+2][1])
fs.append('U15=%s/%s' % (seq[i-2][1], seq[i-1][1]))
fs.append('U16=%s/%s' % (seq[i-1][1], seq[i][1]))
fs.append('U17=%s/%s' % (seq[i][1], seq[i+1][1]))
fs.append('U18=%s/%s' % (seq[i+1][1], seq[i+2][1]))
fs.append('U20=%s/%s/%s' % (seq[i-2][1], seq[i-1][1], seq[i][1]))
fs.append('U21=%s/%s/%s' % (seq[i-1][1], seq[i][1], seq[i+1][1]))
fs.append('U22=%s/%s/%s' % (seq[i][1], seq[i+1][1], seq[i+2][1]))
ret.append("%s\t%s" % (seq[i][2], '\t'.join(fs))) # example-label , followed by feature vector
return ret
def feature_extractor(X):
crfutils.apply_templates(X, templates)
if X:
X[0]['F'].append('__BOS__') # BOS feature
X[-1]['F'].append('__EOS__') # EOS feature
features = OrderedDict()
for i in feature_set:
features[i[0]] = i[1]
for w, _ in features.items():
if w in trigrams:
features[w] = [[[w, -1]], [[w, 0]], [[w, 1]]]
feature_keys = features.keys()
feature_items = features.values()
input_columns = ' '.join(feature_keys) + ' chunk y'
attribute_templates = []
for i in feature_items:
attribute_templates += i
print("Using features: {}.".format(str(attribute_templates)))
feature_extractor = lambda x: crfutils.apply_templates(x, attribute_templates)
for fi, txt in [(train_csv, "train"), (devel_csv, "devel"), (test_csv, "test")]:
write_to = path.join(trigram_path, txt + "_trigrams_" + str(mode) + ".crfsuite")
fo = open(write_to, "w+")
print("Writing to {}...".format(write_to))
crfutils.main(feature_extractor, fi, fo, fields=input_columns, sep='\t')
fo.close()
train_csv.close()
devel_csv.close()
test_csv.close()
def feature_extractor(X):
# Apply attribute templates to obtain features (in fact, attributes)
crfutils.apply_templates(X, templates)