class_docstr: This class is used to prepare and separate word similarity datasets.
class_docstr_tok: ["This","class","is","used","to","prepare","and","separate","word","similarity","datasets","."]
docstr: This function loads the result of a similarity metric for a specific dataset
  
  :param sim_name: the name similarity metric
  :param dataset_name: the name of word similarity dataset
  :return: cor relation score and rating scores generated by similarity metric
docstr_tok: ["This","function","loads","the","result","of","a","similarity","metric","for","a","specific","dataset",":","param","sim_name",":","the","name","similarity","metric",":","param","dataset_name",":","the","name","of","word","similarity","dataset",":","return",":","cor","relation","score","and","rating","scores","generated","by","similarity","metric"]
code: def load_result(self, sim_name, dataset_name):
      """
      This function loads the result of a similarity metric for a specific dataset
  
      :param sim_name: the name similarity metric
      :param dataset_name: the name of word similarity dataset
      :return: cor relation score and rating scores generated by similarity metric
      """
      data = FileIO.read_list_file(
          "dataset/wordsim/results/%s-%s.txt" % (dataset_name, sim_name)
      )
      data = list(map(float, data))
      return data[0], data[1:]
  
imports: from __future__ import division, print_function
  import six
  from scipy.stats import spearmanr
  from scipy.stats import pearsonr
  from sematch.semantic.similarity import YagoTypeSimilarity
  from sematch.utility import FileIO
  import numpy
  from scipy.stats import t, norm
  from math import atanh, pow
  from numpy import tanh
  from sklearn.metrics import classification_report
  from sklearn.metrics import precision_recall_fscore_support
  from sklearn.metrics import accuracy_score
  from nltk import ConfusionMatrix
label_desc: Use image node_id 3 for calling the WordSimDataset obj's underlying member method code with example usage: obj.load_result(sim_name, dataset_name) and returns: data,  data