class_docstr: This class is used to extract nouns, proper nouns, phrases from text, including descriptions and
  url links
class_docstr_tok: ["This","class","is","used","to","extract","nouns",",","proper","nouns",",","phrases","from","text",",","including","descriptions","and","url","links"]
code: def __init__(
      self,
      w_tokenize=None,
      s_tokenize=None,
      pos_tag=None,
      stop_words=None,
      punct=None,
      grammar=chunk_grammar_phrases,
  ):
      self._word_tokenize = w_tokenize if w_tokenize else word_tokenize
      self._sent_tokenize = s_tokenize if s_tokenize else sent_tokenize
      self._tagger = pos_tag if pos_tag else nltk.pos_tag
      self._stop_words = stop_words if stop_words else stopwords
      self._punct = punct if punct else set(string.punctuation)
      self._chunk_grammar = grammar
      self._chunker = nltk.RegexpParser(self._chunk_grammar)
  
imports: from sematch.utility import FileIO
  from nltk.stem import WordNetLemmatizer
  from nltk.corpus import wordnet
  import nltk
  from collections import Counter
  import itertools
  import string
  import re
label_desc: Use image node_id 1 to create a new Extraction object  with example: obj = Extraction(w_tokenize, s_tokenize, pos_tag, stop_words, punct, grammar)