text: from helper import *
  import multiprocessing
  import fasttext.util
  import sys
  import time
  
  numKeywords = int(sys.argv[1])
  vectorSize = int(sys.argv[2])
  maxCandidateArticles = int(sys.argv[3])
  reducedSet = str(sys.argv[4])
  printTimestamp("Getting candidate articles")
  if reducedSet=='true':
      candidate_articles = getCandidateArticles(maxCandidateArticles, True)
  else:
      candidate_articles = getCandidateArticles(maxCandidateArticles, False)
  printTimestamp("Loading FasTextWiki")
  fasttext.util.download_model('en', if_exists='ignore')
  model = fasttext.load_model('cc.en.300.bin')
  
  start = time.time()
  
  for query in range(6,7):
      for keywordExtractor in ["TopicRank", "TfIdf", "KPMiner", "YAKE", "TextRank", "SingleRank", "TopicalPageRank", "PositionRank", "MultipartiteRank"]:
          mp = multiprocessing.Process(target=findSimilarity, args=(keywordExtractor, "FTWiki", model, candidate_articles, query, numKeywords, vectorSize))
          mp.start()
          #mp.join()
  
  end = time.time()
  print('{:.4f} s'.format(end - start))
  
  
  