def tokenize(self, docs):
""" Tokenizes a document, using a lemmatizer.
Args:
| doc (str) -- the text document to process.
Returns:
| list -- the list of tokens.
"""
tokens = []
for doc in docs:
toks = []
for t in spacy(doc, tag=True, parse=False, entity=False):
token = t.lower_.strip()
tag = t.tag_
# Ignore stopwords
if token in self.stops:
continue
# Lemmatize
wn_tag = penn_to_wordnet(tag)
if wn_tag is not None:
lemma = self.lemmr.lemmatize(token, wn_tag)
toks.append(lemma)
else:
toks.append(token)
tokens.append(toks)
return tokens
def tokenize(self, docs):
if self.lemmatize:
lem = WordNetLemmatizer()
pre_tdocs = RAKE().tokenize(docs)
tdocs = []
for i, tdoc in enumerate(pre_tdocs):
# Split phrase keywords into 1gram keywords,
# to check tokens against
kws_1g = [t.split(' ') for t in tdoc]
kws_1g = [kw for grp in kws_1g for kw in grp]
toks = spacy(docs[i], tag=True, parse=False, entity=False)
tagged = [(t.lower_.strip(), t.tag_) for t in toks]
toks = []
for tok, tag in tagged:
if tok in kws_1g:
wn_tag = penn_to_wordnet(tag)
if wn_tag is not None:
toks.append(lem.lemmatize(tok, wn_tag))
tdocs.append(toks)
tdocs = extract_phrases(tdocs, docs)
if prune:
return prune(tdocs)
return tdocs
def tokenize(self, docs):
tags = ['NN', 'NNS', 'NNP', 'NNPS']
keywords = []
for doc in docs:
toks = spacy(doc, tag=True, parse=False, entity=False)
tagged = [(t.lower_.strip(), t.tag_) for t in toks]
kws = [t for t, tag in tagged if tag in tags]
kws += extract_noun_phrases(tagged)
keywords.append(kws)
return prune(keywords)
def _process_doc(self, doc):
"""
Applies DCS to a document to extract its core concepts and their weights.
"""
# Prep
doc = doc.lower()
tagged_tokens = [(t, penn_to_wordnet(t.tag_)) for t in spacy(doc, tag=True, parse=False, entity=False)]
tokens = [t for t, tag in tagged_tokens]
term_concept_map = self._disambiguate_doc(tagged_tokens)
concept_weights = self._weight_concepts(tokens, term_concept_map)
# Compute core semantics
lexical_chains = self._lexical_chains(doc, term_concept_map)
core_semantics = self._core_semantics(lexical_chains, concept_weights)
core_concepts = [c for chain in core_semantics for c in chain]
return [(con, concept_weights[con]) for con in core_concepts]
def pre_tokenize(doc, tdoc, lem):
# Split phrase keywords into 1gram keywords,
# to check tokens against
# We learn keyphrases later on.
kws_1g = [t.split(' ') for t in tdoc]
kws_1g = [kw for grp in kws_1g for kw in grp]
toks = spacy(doc, tag=True, parse=False, entity=False)
tagged = [(t.lower_.strip(), t.tag_) for t in toks]
toks = []
for tok, tag in tagged:
if tok in kws_1g:
wn_tag = penn_to_wordnet(tag)
if wn_tag is not None:
toks.append(lem.lemmatize(tok, wn_tag))
return toks
def _tokenize(self, doc):
toks = []
for t in spacy(doc, tag=True, parse=False, entity=False):
token = t.lower_.strip()
tag = t.tag_
# Ignore stopwords
if token in self.stops:
continue
# Lemmatize
wn_tag = penn_to_wordnet(tag)
if wn_tag is not None:
lemma = self.lemmr.lemmatize(token, wn_tag)
toks.append(lemma)
else:
toks.append(token)
return toks
def _process_doc(self, doc):
"""
Applies DCS to a document to extract its core concepts and their weights.
"""
# Prep
doc = doc.lower()
tagged_tokens = [
(t, penn_to_wordnet(t.tag_))
for t in spacy(doc, tag=True, parse=False, entity=False)
]
tokens = [t for t, tag in tagged_tokens]
term_concept_map = self._disambiguate_doc(tagged_tokens)
concept_weights = self._weight_concepts(tokens, term_concept_map)
# Compute core semantics
lexical_chains = self._lexical_chains(doc, term_concept_map)
core_semantics = self._core_semantics(lexical_chains, concept_weights)
core_concepts = [c for chain in core_semantics for c in chain]
return [(con, concept_weights[con]) for con in core_concepts]
