docstr: Compute context and candidate similarity using maximun word-category similarity
  :param context:
  :param candidates:
  :param similarity: sim(word, cat)
  :param K:
  :return:
code: def category_disambiguate_max(
      self, context, candidates, similarity, K=10
  ):
      """
      Compute context and candidate similarity using maximun word-category similarity
      :param context:
      :param candidates:
      :param similarity: sim(word, cat)
      :param K:
      :return:
      """
      result = {}
      context_words = self._extractor.context_features(context)
      candidate_features = self._extractor.entity_categories(candidates)
      for candidate, features in candidate_features.iteritems():
          score = {}
          for x in context_words:
              for y in features:
                  score[(x, y)] = similarity(x, y)
          topk = Counter(score).most_common(K)
          if topk:
              word, scores = zip(*topk)
              result[candidate] = sum(scores)
          else:
              result[candidate] = -1.0
      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 5 for calling the EntityDisambiguation obj's underlying member method code with example usage: obj.category_disambiguate_max(context, candidates, similarity, K) and returns: Counter