class_docstr: Implementation of RAKE -- Rapid Automatic Keywords Extraction From Individual Documents
  https://github.com/aneesha/RAKE/blob/master/rake.py
  
  To make a class for rake python implementation for easy incorporation in other modules.
  This class is used to extract keywords from textual document, such as description of entity from DBpedia.
class_docstr_tok: ["Implementation","of","RAKE","--","Rapid","Automatic","Keywords","Extraction","From","Individual","Documents","https",":","\/\/github.com\/aneesha\/RAKE\/blob\/master\/rake.py","To","make","a","class","for","rake","python","implementation","for","easy","incorporation","in","other","modules",".","This","class","is","used","to","extract","keywords","from","textual","document",",","such","as","description","of","entity","from","DBpedia","."]
code: def ranking_phrases(self, phrases):
      word_frequency = {}
      word_degree = {}
      for p in phrases:
          words = self._word_tokenize(p)
          degree = len(words) - 1
          for w in words:
              word_frequency.setdefault(w, 0)
              word_frequency[w] += 1
              word_degree.setdefault(w, 0)
              word_degree[w] += degree
  
      for word in word_frequency:
          word_degree[word] += word_frequency[word]
  
      word_scorer = lambda x: word_degree[x] / 1.0 * word_frequency[x]
      word_score = {word: word_scorer(word) for word in word_frequency}
  
      phrase_scorer = lambda x: sum(
          [word_score[word] for word in self._word_tokenize(x)]
      )
      phrase_score = {p: phrase_scorer(p) for p in phrases}
  
      return phrase_score
  
imports: from sematch.utility import FileIO
  from nltk.stem import WordNetLemmatizer
  from nltk.corpus import wordnet
  import nltk
  from collections import Counter
  import itertools
  import string
  import re
label_desc: Use image node_id 5 for calling the RAKE obj's underlying member method code with example usage: obj.ranking_phrases(phrases) and returns: phrase_score