def __init__(self,
tokenizer=None,
pos_tagger=None,
np_extractor=None,
analyzer=None,
parser=None,
classifier=None):
self.tokenizer = tokenizer if tokenizer is not None else NLTKPunktTokenizer()
self.pos_tagger = pos_tagger if pos_tagger is not None \
else PatternTagger(tokenizer=self.tokenizer)
self.np_extractor = np_extractor if np_extractor is not None \
else PatternParserNPExtractor(tokenizer=self.tokenizer)
self.analyzer = analyzer if analyzer is not None \
else PatternAnalyzer(tokenizer=self.tokenizer)
self.parser = parser if parser is not None \
else PatternParser(tokenizer=self.tokenizer)
self.classifier = classifier if classifier is not None else None
_initialize_models(
self,
self.tokenizer,
self.pos_tagger,
self.np_extractor,
self.analyzer,
self.parser,
self.classifier)
def __init__(self,
tokenizer=None,
pos_tagger=None,
np_extractor=None,
analyzer=None,
parser=None,
classifier=None):
self.tokenizer = tokenizer if tokenizer is not None else NLTKPunktTokenizer()
self.pos_tagger = pos_tagger if pos_tagger is not None \
else PatternTagger(tokenizer=self.tokenizer)
self.np_extractor = np_extractor if np_extractor is not None \
else PatternParserNPExtractor(tokenizer=self.tokenizer)
self.analyzer = analyzer if analyzer is not None \
else PatternAnalyzer(tokenizer=self.tokenizer)
self.parser = parser if parser is not None \
else PatternParser(tokenizer=self.tokenizer)
self.classifier = classifier if classifier is not None else None
_initialize_models(
self,
self.tokenizer,
self.pos_tagger,
self.np_extractor,
self.analyzer,
self.parser,
self.classifier)
def __init__(self,
text,
tokenizer=None,
pos_tagger=None,
np_extractor=None,
analyzer=None,
parser=None,
classifier=None,
clean_html=False):
self.tokenizer = tokenizer if tokenizer is not None else NLTKPunktTokenizer(
)
self.pos_tagger = pos_tagger if pos_tagger is not None else PatternTagger(
tokenizer=self.tokenizer)
self.np_extractor = np_extractor if np_extractor is not None \
else PatternParserNPExtractor(tokenizer=self.tokenizer)
self.analyzer = analyzer if analyzer is not None \
else PatternAnalyzer(tokenizer=self.tokenizer)
self.parser = parser if parser is not None \
else PatternParser(tokenizer=self.tokenizer)
self.classifier = classifier if classifier is not None else None
if not isinstance(text, basestring):
raise TypeError('The `text` argument passed to `__init__(text)` '
'must be a string, not {0}'.format(type(text)))
if clean_html:
raise NotImplementedError(
"clean_html has been deprecated. "
"To remove HTML markup, use BeautifulSoup's "
"get_text() function")
self.raw = self.string = text
self.stripped = lowerstrip(self.raw, all=True)
_initialize_models(self, self.tokenizer, self.pos_tagger,
self.np_extractor, self.analyzer, self.parser,
self.classifier)
def __init__(self, text, tokenizer=None,
pos_tagger=None,
np_extractor=None,
analyzer=None,
parser=None,
classifier=None, clean_html=False):
self.tokenizer = tokenizer if tokenizer is not None else NLTKPunktTokenizer()
self.pos_tagger = pos_tagger if pos_tagger is not None else PatternTagger(
tokenizer=self.tokenizer)
self.np_extractor = np_extractor if np_extractor is not None \
else PatternParserNPExtractor(tokenizer=self.tokenizer)
self.analyzer = analyzer if analyzer is not None \
else PatternAnalyzer(tokenizer=self.tokenizer)
self.parser = parser if parser is not None \
else PatternParser(tokenizer=self.tokenizer)
self.classifier = classifier if classifier is not None else None
if not isinstance(text, basestring):
raise TypeError('The `text` argument passed to `__init__(text)` '
'must be a string, not {0}'.format(type(text)))
if clean_html:
raise NotImplementedError(
"clean_html has been deprecated. "
"To remove HTML markup, use BeautifulSoup's "
"get_text() function")
self.raw = self.string = text
self.stripped = lowerstrip(self.raw, all=True)
_initialize_models(
self,
self.tokenizer,
self.pos_tagger,
self.np_extractor,
self.analyzer,
self.parser,
self.classifier)
