class_docstr: This class contains TF-IDF model and LSA model of text-text similarity.
class_docstr_tok: ["This","class","contains","TF-IDF","model","and","LSA","model","of","text-text","similarity","."]
code: def __init__(
      self,
      DICT="models/abstract/abstracts.dict",
      TFIDF_MODEL="models/abstract/abstracts_tfidf.model",
      LSA_MODEL="models/abstract/abstracts_lsi.model",
  ):
      try:
          from nltk.tokenize import RegexpTokenizer
          from nltk.stem import WordNetLemmatizer
          import nltk
  
          self._tokenizer = RegexpTokenizer(r"[a-z]+")
          self._lemma = WordNetLemmatizer()
          self._stopwords = set(nltk.corpus.stopwords.words("english"))
      except:
          print("Install NLTK and download WordNet!")
          import sys
  
          sys.exit()
      try:
          from gensim import corpora, models
          from sematch.utility import FileIO
  
          self._dict = corpora.Dictionary.load(FileIO.filename(DICT))
          self._tfidf = models.TfidfModel.load(
              FileIO.filename(TFIDF_MODEL)
          )
          self._lsa = models.LsiModel.load(FileIO.filename(LSA_MODEL))
      except Exception as ex:
          print("Error loading gensim: ", ex)
          print("Install gensim and prepare models data!")
          import sys
  
          sys.exit()
  
imports: from numpy import array
  from gensim import matutils
label_desc: Use image node_id 1 to create a new TextRelatedness object  with example: obj = TextRelatedness(DICT, TFIDF_MODEL, LSA_MODEL)