code: def extract_meaningful_words_from_sentence(sentence):
      """
      extract meaningful (nouns and verbs) words from sentence
  
      Args:
          sentence(str): input sentence
  
      Returns:
          list(str): vector of meaningful words
  
      """
      sentence = re.sub(r"[-+.^:,\[\]()]", "", str(sentence))
  
      tokens = nltk.word_tokenize(sentence)
      pos_tags = nltk.pos_tag(tokens)
      words_vec = []
      cntr = 0
      for word, tag in pos_tags:
          if tag.startswith("NN") | tag.startswith("VB"):
              if not bool(
                  re.search(r"\d", word)
              ):  # disqualify words does not contain digits
                  words_vec.insert(cntr, word)
                  cntr += 1
  
      return words_vec
  
file_path: nlp-architect/examples/most_common_word_sense/feature_extraction.py
imports: import re
  import nltk
  import numpy
  from numpy import dot
  from numpy.linalg import norm
  from nlp_architect.utils.generic import license_prompt
label_desc: Use image node_id 2 for calling a global function with example usage: extract_meaningful_words_from_sentence(sentence) and returns: words_vec