class_docstr: ISRI Arabic stemmer based on algorithm: Arabic Stemming without a root dictionary.
  Information Science Research Institute. University of Nevada, Las Vegas, USA.
  
  A few minor modifications have been made to ISRI basic algorithm.
  See the source code of this module for more information.
  
  isri.stem(token) returns Arabic root for the given token.
  
  The ISRI Stemmer requires that all tokens have Unicode string types.
  If you use Python IDLE on Arabic Windows you have to decode text first
  using Arabic '1256' coding.
class_docstr_tok: ["ISRI","Arabic","stemmer","based","on","algorithm",":","Arabic","Stemming","without","a","root","dictionary",".","Information","Science","Research","Institute",".","University","of","Nevada",",","Las","Vegas",",","USA",".","A","few","minor","modifications","have","been","made","to","ISRI","basic","algorithm",".","See","the","source","code","of","this","module","for","more","information",".","isri.stem","(","token",")","returns","Arabic","root","for","the","given","token",".","The","ISRI","Stemmer","requires","that","all","tokens","have","Unicode","string","types",".","If","you","use","Python","IDLE","on","Arabic","Windows","you","have","to","decode","text","first","using","Arabic","'1256","'","coding","."]
docstr: process length five patterns and extract length three roots
docstr_tok: ["process","length","five","patterns","and","extract","length","three","roots"]
code: def pro_w53(self, word):
      """process length five patterns and extract length three roots"""
      if (
          word[2] in self.pr53[0] and word[0] == "\u0627"
      ):  # افتعل - افاعل
          word = word[1] + word[3:]
      elif (
          word[3] in self.pr53[1] and word[0] == "\u0645"
      ):  # مفعول - مفعال - مفعيل
          word = word[1:3] + word[4]
      elif (
          word[0] in self.pr53[2] and word[4] == "\u0629"
      ):  # مفعلة - تفعلة - افعلة
          word = word[1:4]
      elif (
          word[0] in self.pr53[3] and word[2] == "\u062a"
      ):  # مفتعل - يفتعل - تفتعل
          word = word[1] + word[3:]
      elif (
          word[0] in self.pr53[4] and word[2] == "\u0627"
      ):  # مفاعل - تفاعل
          word = word[1] + word[3:]
      elif (
          word[2] in self.pr53[5] and word[4] == "\u0629"
      ):  # فعولة - فعالة
          word = word[:2] + word[3]
      elif (
          word[0] in self.pr53[6] and word[1] == "\u0646"
      ):  # انفعل - منفعل
          word = word[2:]
      elif word[3] == "\u0627" and word[0] == "\u0627":  # افعال
          word = word[1:3] + word[4]
      elif word[4] == "\u0646" and word[3] == "\u0627":  # فعلان
          word = word[:3]
      elif word[3] == "\u064a" and word[0] == "\u062a":  # تفعيل
          word = word[1:3] + word[4]
      elif word[3] == "\u0648" and word[1] == "\u0627":  # فاعول
          word = word[0] + word[2] + word[4]
      elif word[2] == "\u0627" and word[1] == "\u0648":  # فواعل
          word = word[0] + word[3:]
      elif word[3] == "\u0626" and word[2] == "\u0627":  # فعائل
          word = word[:2] + word[4]
      elif word[4] == "\u0629" and word[1] == "\u0627":  # فاعلة
          word = word[0] + word[2:4]
      elif word[4] == "\u064a" and word[2] == "\u0627":  # فعالي
          word = word[:2] + word[3]
      else:
          word = self.suf1(word)  # do - normalize short sufix
          if len(word) == 5:
              word = self.pre1(word)  # do - normalize short prefix
      return word
  
file_path: nltk/nltk/stem/isri.py
imports: import re
  from nltk.stem.api import StemmerI
label_desc: Use image node_id 8 for calling the ISRIStemmer obj's underlying member method code with example usage: obj.pro_w53(word) and returns: word