def __init__(self):
self.splitter = Splitter()
self.postagger = POSTagger()
self.dicttagger = DictionaryTagger([
'/home/msinghal/PycharmProjects/basic_sentiment_analysis/dicts/positive.yml',
'/home/msinghal/PycharmProjects/basic_sentiment_analysis/dicts/negative.yml',
'/home/msinghal/PycharmProjects/basic_sentiment_analysis/dicts/morePositive.yml',
'/home/msinghal/PycharmProjects/basic_sentiment_analysis/dicts/moreNegative.yml',
'/home/msinghal/PycharmProjects/basic_sentiment_analysis/dicts/invert.yml'
])
def processQuestion(gloveModel,
question,
minLen=1,
maxLen=3,
useAPI=False,
useSynonyms=False):
tagger = POSTagger()
pos = tagger.parse(question)
# create splitter and generalizer
splitter = Splitter()
if question[-1] == '?' or question[-1] == '.':
question = question[:-1]
gen_question = splitter.generalize(question, pos)
labels = []
resultsExists = False
if not useAPI:
parts = list(splitter.split(gen_question, min=minLen, max=maxLen))
else:
resultsExists = True
apiResult, _ = api.getBinaryRelations(question)
parts = [
rel.predicate for rel in apiResult
if len(rel.predicate_positions_) > 1
]
for part in parts:
if len(part.split()) > 1:
labels.append(part.split()[0] +
''.join(''.join([w[0].upper(), w[1:].lower()])
for w in part.split()[1:]))
if useSynonyms:
predicates = [max(part.split(), key=len) for part in parts]
if predicates is not None and len(predicates) > 0:
for predicate in predicates:
for part in list(parts):
if predicate in part:
for syn in gloveModel.gloveModel.most_similar(
predicate.lower()):
parts.append(part.replace(predicate, syn[0]))
if len(parts) == 0:
resultsExists = False
parts = list(splitter.split(gen_question, min=minLen, max=maxLen))
# create embedder part
vectors = []
for part in parts:
vectors.append(gloveModel.getVector(part))
return vectors, parts, pos, gen_question, labels, resultsExists
def extract_text(arg, punkt):
"""
Extract sentences from argument text and find the passive sentences
"""
with open(arg) as f:
logger.info("Reading Text file")
text = f.read()
sentences = punkt.tokenize(text)
logger.info(f"{len(sentences)} sentences detected")
for sent in sentences:
find_passives(sent)
print("-" * 60)
if __name__ == "__main__":
TAGGER = POSTagger().get()
if len(sys.argv) > 1:
# pre-trained version of PunktSentenceTokenizer
punkt = nltk.tokenize.punkt.PunktSentenceTokenizer()
for arg in sys.argv[1:]:
extract_text(arg, punkt)
else:
print("No sentences")
