def __init__(self):
self.grammar = GrammarCorrector()
self.spelling = SpellCorrector()
self.stem = stem
def __init__(self):
self.grammar = GrammarCorrector()
self.spelling = SpellCorrector()
self.stem = stem
class MetaFeatureGenerator(object):
speech_parts = dict(
nouns=["NN", "NNP", "NNPS", "NNS"],
verbs=["VB", "VBD", "VBG", "VBN", "VBP", "VBZ"],
adjectives=["JJ", "JJR", "JJS"],
adverbs=["RB", "RBR", "RBS"]
)
def __init__(self):
self.grammar = GrammarCorrector()
self.spelling = SpellCorrector()
self.stem = stem
def generate_grammar_features(self, raw_grammar):
grammar_counts = {}
for k in self.speech_parts:
grammar_counts[k] = 0
for t in raw_grammar:
for k in self.speech_parts:
if t[1] in self.speech_parts[k]:
grammar_counts[k] += 1
for k in grammar_counts:
grammar_counts[k] /= (len(raw_grammar)+1)
return grammar_counts
def clean_spell_corrected_tags(self, spelling_markup):
return re.sub("<[^>]+>", '', spelling_markup)
def generate_text_features(self, text):
feats = {}
feats['length'] = len(text)
feats['word_length'] = len(text.split())
feats['sentence_length'] = len(text.split("."))
feats['chars_per_sentence'] = feats['length'] / (feats['sentence_length'] + 1)
feats['words_per_sentence'] = feats['word_length'] / (feats['sentence_length'] + 1)
feats['chars_per_word'] = feats['length'] / (feats['word_length'] + 1)
return feats
def generate_clean_stem_text(self, text):
spelling_errors, spelling_markup, raw_spelling = self.spelling.correct_string(text)
clean_text = self.clean_spell_corrected_tags(spelling_markup)
clean_text = re.sub("[^A-Za-z0-9 \.,\'\":;]", " ", clean_text.lower())
clean_text = re.sub("\s+", " ", clean_text)
clean_text = ' '.join([self.stem(t) for t in clean_text.split(' ')])
return clean_text
def generate_meta_features(self, text):
features = {}
grammar_errors, grammar_markup, raw_grammar = self.grammar.correct_string(text)
features['grammar_errors'] = grammar_errors
grammar_feats = self.generate_grammar_features(raw_grammar)
features.update(grammar_feats)
spelling_errors, spelling_markup, raw_spelling = self.spelling.correct_string(text)
features['spelling_errors'] = spelling_errors
text_feats = self.generate_text_features(text)
features.update(text_feats)
return features
class MetaFeatureGenerator(object):
speech_parts = dict(nouns=["NN", "NNP", "NNPS", "NNS"],
verbs=["VB", "VBD", "VBG", "VBN", "VBP", "VBZ"],
adjectives=["JJ", "JJR", "JJS"],
adverbs=["RB", "RBR", "RBS"])
def __init__(self):
self.grammar = GrammarCorrector()
self.spelling = SpellCorrector()
self.stem = stem
def generate_grammar_features(self, raw_grammar):
grammar_counts = {}
for k in self.speech_parts:
grammar_counts[k] = 0
for t in raw_grammar:
for k in self.speech_parts:
if t[1] in self.speech_parts[k]:
grammar_counts[k] += 1
for k in grammar_counts:
grammar_counts[k] /= len(raw_grammar)
return grammar_counts
def clean_spell_corrected_tags(self, spelling_markup):
return re.sub("<[^>]+>", '', spelling_markup)
def generate_text_features(self, text):
feats = {}
feats['length'] = len(text)
feats['word_length'] = len(text.split())
feats['sentence_length'] = len(text.split("."))
feats['chars_per_sentence'] = feats['length'] / (
feats['sentence_length'] + 1)
feats['words_per_sentence'] = feats['word_length'] / (
feats['sentence_length'] + 1)
feats['chars_per_word'] = feats['length'] / (feats['word_length'] + 1)
return feats
def generate_clean_stem_text(self, text):
spelling_errors, spelling_markup, raw_spelling = self.spelling.correct_string(
text)
clean_text = self.clean_spell_corrected_tags(spelling_markup)
clean_text = re.sub("[^A-Za-z0-9 \.,\'\":;]", " ", clean_text.lower())
clean_text = re.sub("\s+", " ", clean_text)
clean_text = ' '.join([self.stem(t) for t in clean_text.split(' ')])
return clean_text
def generate_meta_features(self, text):
features = {}
grammar_errors, grammar_markup, raw_grammar = self.grammar.correct_string(
text)
features['grammar_errors'] = grammar_errors
grammar_feats = self.generate_grammar_features(raw_grammar)
features.update(grammar_feats)
spelling_errors, spelling_markup, raw_spelling = self.spelling.correct_string(
text)
features['spelling_errors'] = spelling_errors
text_feats = self.generate_text_features(text)
features.update(text_feats)
return features
