Generate MCQ Function

generate_MCQ.py

import requests
import json
import re
import random
from pywsd.similarity import max_similarity
from pywsd.lesk import adapted_lesk
from pywsd.lesk import simple_lesk
from pywsd.lesk import cosine_lesk
from nltk.corpus import wordnet as wn

# Distractors from Wordnet
def get_distractors_wordnet(syn,word):
    distractors=[]
    word= word.lower()
    orig_word = word
    if len(word.split())>0:
        word = word.replace(" ","_")
    hypernym = syn.hypernyms()
    if len(hypernym) == 0: 
        return distractors
    for item in hypernym[0].hyponyms():
        name = item.lemmas()[0].name()
        #print ("name ",name, " word",orig_word)
        if name == orig_word:
            continue
        name = name.replace("_"," ")
        name = " ".join(w.capitalize() for w in name.split())
        if name is not None and name not in distractors:
            distractors.append(name)
    return distractors

def get_wordsense(sent,word):
    word= word.lower()
    
    if len(word.split())>0:
        word = word.replace(" ","_")
    
    
    synsets = wn.synsets(word,'n')
    if synsets:
        wup = max_similarity(sent, word, 'wup', pos='n')
        adapted_lesk_output =  adapted_lesk(sent, word, pos='n')
        lowest_index = min (synsets.index(wup),synsets.index(adapted_lesk_output))
        return synsets[lowest_index]
    else:
        return None

# Distractors from http://conceptnet.io/
def get_distractors_conceptnet(word):
    word = word.lower()
    original_word= word
    if (len(word.split())>0):
        word = word.replace(" ","_")
    distractor_list = [] 
    url = "http://api.conceptnet.io/query?node=/c/en/%s/n&rel=/r/PartOf&start=/c/en/%s&limit=5"%(word,word)
    obj = requests.get(url).json()

    for edge in obj['edges']:
        link = edge['end']['term'] 

        url2 = "http://api.conceptnet.io/query?node=%s&rel=/r/PartOf&end=%s&limit=10"%(link,link)
        obj2 = requests.get(url2).json()
        for edge in obj2['edges']:
            word2 = edge['start']['label']
            if word2 not in distractor_list and original_word.lower() not in word2.lower():
                distractor_list.append(word2)
                   
    return distractor_list

key_distractor_list = {}

for keyword in keyword_sentence_mapping:
    wordsense = get_wordsense(keyword_sentence_mapping[keyword][0],keyword)
    if wordsense:
        distractors = get_distractors_wordnet(wordsense,keyword)
        if len(distractors) ==0:
            distractors = get_distractors_conceptnet(keyword)
        if len(distractors) != 0:
            key_distractor_list[keyword] = distractors
    else:
        
        distractors = get_distractors_conceptnet(keyword)
        if len(distractors) != 0:
            key_distractor_list[keyword] = distractors

index = 1
print ("#############################################################################")
print ("NOTE::::::::  Since the algorithm might have errors along the way, wrong answer choices generated might not be correct for some questions. ")
print ("#############################################################################\n\n")
for each in key_distractor_list:
    sentence = keyword_sentence_mapping[each][0]
    pattern = re.compile(each, re.IGNORECASE)
    output = pattern.sub( " _______ ", sentence)
    print ("%s)"%(index),output)
    choices = [each.capitalize()] + key_distractor_list[each]
    top4choices = choices[:4]
    random.shuffle(top4choices)
    optionchoices = ['a','b','c','d']
    for idx,choice in enumerate(top4choices):
        print ("\t",optionchoices[idx],")"," ",choice)
    print ("\nMore options: ", choices[4:20],"\n\n")
    index = index + 1
    

Hii, I was trying this code for generating mcq but I don't know why it is generating an error and I couldn't find an answer to that. I'll be really glad if you could help me solve it.

IndexError Traceback (most recent call last)
Input In [111], in <cell line: 72>()
key_distractor_list = {}
for keyword in keyword_sentence_mapping:
---> wordsense = get_wordsense(keyword_sentence_mapping[keyword][0],keyword)
if wordsense:
distractors = get_distractors_wordnet(wordsense,keyword)

IndexError: list index out of range

@shreykul Where you able to get a soultion for this?

@nithin13231323 I was able to run it at that time but I don't remember now how i did it... I'll try again and see if it works now.

@shreykul ok let me know if you got the solution for that I think it’s some problem with the limit of keywords on the text or something just let me know if you find it