class_docstr: This class implements similarity based category classifiers.
class_docstr_tok: ["This","class","implements","similarity","based","category","classifiers","."]
docstr: Compute the semantic similarity between a word and a category.
  :param word: a feature word
  :param category: predefined category
  :param method: the name of semantic similarity metric
  :return: similarity score between word and category
docstr_tok: ["Compute","the","semantic","similarity","between","a","word","and","a","category",".",":","param","word",":","a","feature","word",":","param","category",":","predefined","category",":","param","method",":","the","name","of","semantic","similarity","metric",":","return",":","similarity","score","between","word","and","category"]
code: def category_similarity(self, word, category):
      """
      Compute the semantic similarity between a word and a category.
      :param word: a feature word
      :param category: predefined category
      :param method: the name of semantic similarity metric
      :return: similarity score between word and category
      """
      return self._sim_model(word, category)
  
imports: from sematch.semantic.sparql import NameSPARQL, QueryGraph
  from sematch.semantic.similarity import YagoTypeSimilarity
  from sematch.utility import memoized
  from sematch.nlp import word_tokenize, word_process, Extraction
  import numpy
  import itertools
  from collections import Counter
  import six
  from six.moves import xrange
  from sklearn.svm import LinearSVC
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import LabelEncoder
  from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
  from sklearn.feature_extraction import DictVectorizer
  import time
label_desc: Use image node_id 7 for calling the SimClassifier obj's underlying member method code with example usage: obj.category_similarity(word, category) and returns: self