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import nltk
nltk.download('stopwords')
nltk.download('wordnet')
nltk.download('punkt')
from nltk.corpus import stopwords,wordnet
from nltk.tokenize import sent_tokenize
import string
import subprocess
import logging
try:
import pke
logging.error("importing pke info")
except:
logging.error("installing pke info")
subprocess.run(['pip3', 'install','git+https://github.com/boudinfl/pke.git'])
subprocess.run(['python3' ,'-m' ,'spacy' ,'download' ,'en'])
import pke
stoplist = list(string.punctuation)
stoplist += pke.lang.stopwords.get('en')
stoplist += ['-lrb-', '-rrb-', '-lcb-', '-rcb-', '-lsb-', '-rsb-']
stoplist += stopwords.words('english')
def tokenize_sentence(text):
sentences=sent_tokenize(text)
sentences=[s.strip().lstrip().rstrip() for s in sentences if len(s) > 20]
return sentences
def get_multipartiterank_topics(text):
output = []
try:
extractor = pke.unsupervised.MultipartiteRank()
extractor.load_document(input=text, language='en',normalization=None,stoplist=stoplist)
# keyphrase candidate selection #'ADJ' 'ADP' 'ADV' 'AUX' 'DET' 'NOUN' 'NUM' 'PART' 'PROPN' 'PUNCT' 'VERB'
extractor.candidate_selection(pos={'NOUN','VERB','ADJ'})
extractor.candidate_weighting(threshold=0.7,method='average',alpha=1.1)
keyphrases = extractor.get_n_best(n=5)
for val in keyphrases:
output.append(val[0])
except Exception as e:
print("found exception",e)
return list(set(output))
def get_topicrank_topics(text):
output = []
try:
extractor = pke.unsupervised.TopicRank()
extractor.load_document(input=text, language='en',normalization=None,stoplist=stoplist)
# keyphrase candidate selection #'ADJ' 'ADP' 'ADV' 'AUX' 'DET' 'NOUN' 'NUM' 'PART' 'PROPN' 'PUNCT' 'VERB'
extractor.candidate_selection(pos={'NOUN', 'ADJ'})
extractor.candidate_weighting(threshold=0.7,method='average')
keyphrases = extractor.get_n_best(n=5)
for val in keyphrases:
output.append(val[0])
except Exception as e:
print("found exception",e)
return list(set(output))
def get_yake_topics(text):
#statistics model --very poor performance
output = []
try:
extractor = pke.unsupervised.YAKE()
extractor.load_document(input=text, language='en',normalization=None,stoplist=stoplist)
extractor.candidate_selection(n=3)
extractor.candidate_weighting(window=2)
keyphrases = extractor.get_n_best(n=5,threshold=0.9)
for val in keyphrases:
output.append(val[0])
except Exception as e:
print("found exception",e)
return list(set(output))
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