docstr: Compute context and candidate similarity using text relatedness
  :param context:
  :param candidates:
  :param similarity: TFIDF or LSA
  :return:
code: def text_disambiguate(self, context, candidates, similarity):
      """
      Compute context and candidate similarity using text relatedness
      :param context:
      :param candidates:
      :param similarity: TFIDF or LSA
      :return:
      """
      result = {}
      context_words = " ".join(
          self._extractor.context_features(context)
      )
      candidate_features = self._extractor.entity_descriptions(
          candidates
      )
      for candidate, feature in candidate_features.iteritems():
          result[candidate] = similarity(context_words, feature)
      return Counter(result).most_common(1)[0][0]
  
file_path: sematch/sematch/nel.py
imports: from collections import Counter
  from sematch.nlp import word_tokenize
  import math
label_desc: Use image node_id 3 for calling the EntityDisambiguation obj's underlying member method code with example usage: obj.text_disambiguate(context, candidates, similarity) and returns: Counter