إنشاء أسئلة الاختيار من متعدد (MCQs) تلقائيًا من أي محتوى باستخدام الذكاء الاصطناعي

The Greek historian knew what he was talking about. The Nile River fed Egyptian civilization for hundreds of years. The Longest River the Nile is 4,160 miles long — the world’s longest river. It begins near the equator in Africa and flows north to the Mediterranean Sea ..............

 1) The Nile provided so well for  _______  that sometimes they had surpluses, or more goods than they needed.
	 a )   Angolan
	 b )   Algerian
	 c )   Egyptians
	 d )   Bantu

More options:  ['Basotho', 'Beninese', 'Berber', 'Black African', 'Burundian', 'Cameroonian', 'Carthaginian', 'Chadian', 'Chewa', 'Congolese', 'Djiboutian', 'Egyptian', 'Ethiopian', 'Eurafrican', 'Ewe', 'Fulani'] 


2) As in many ancient societies, much of the knowledge of  _______  came about as priests studied the world to find ways to please the gods.
	 a )   Malawi
	 b )   East Africa
	 c )   Somalia
	 d )   Egypt

More options:  ['Togo', 'Zimbabwe', 'Gabon', 'Ghana', 'Lake Tanganyika', 'Ottoman Empire', 'Mozambique', 'Iran', 'Israel', 'Saudi Arabia', 'Lebanon', 'Turkey', 'Iraq', 'Levant', 'Syria', 'Jordan'] 


3) The  _______  provided so well for Egyptians that sometimes they had surpluses, or more goods than they needed.
	 a )   Nyala
	 b )   Omdurman
	 c )   Nile
	 d )   Port Sudan

More options:  ['Khartoum', 'Nubian Desert', 'Darfur', 'Libyan Desert', 'Kordofan', 'Gulu', 'Buganda', 'Entebbe', 'Jinja', 'Lake Edward', 'entebbe', 'gulu', 'kayunga', 'Upper Egypt', 'Suez Canal', 'Aswan High Dam']

 !pip install gensim
!pip install git+https://github.com/boudinfl/pke.git
!python -m spacy download en
!pip install bert-extractive-summarizer --upgrade --force-reinstall
!pip install spacy==2.1.3 --upgrade --force-reinstall
!pip install -U nltk
!pip install -U pywsd
import nltk
nltk.download('stopwords')
nltk.download('popular')

from summarizer import Summarizer
f = open("egypt.txt","r")
full_text = f.read()
model = Summarizer()
result = model(full_text, min_length=60, max_length = 500 , ratio = 0.4)
summarized_text = ''.join(result)
print (summarized_text)

The Nile River fed Egyptian civilization for hundreds of years. It begins near the equator in Africa and flows north to the Mediterranean Sea. A delta is an area near a river’s mouth where the water deposits fine soil called silt.......................

import pprint
import itertools
import re
import pke
import string
from nltk.corpus import stopwords
def get_nouns_multipartite(text):
    out=[]
extractor = pke.unsupervised.MultipartiteRank()
    extractor.load_document(input=text)
    #    not contain punctuation marks or stopwords as candidates.
    pos = {'PROPN'}
    #pos = {'VERB', 'ADJ', 'NOUN'}
    stoplist = list(string.punctuation)
    stoplist += ['-lrb-', '-rrb-', '-lcb-', '-rcb-', '-lsb-', '-rsb-']
    stoplist += stopwords.words('english')
    extractor.candidate_selection(pos=pos, stoplist=stoplist)
    # 4. build the Multipartite graph and rank candidates using random walk,
    #    alpha controls the weight adjustment mechanism, see TopicRank for
    #    threshold/method parameters.
    extractor.candidate_weighting(alpha=1.1,
                                  threshold=0.75,
                                  method='average')
    keyphrases = extractor.get_n_best(n=20)
for key in keyphrases:
        out.append(key[0])
return out
keywords = get_nouns_multipartite(full_text) 
print (keywords)
filtered_keys=[]
for keyword in keywords:
    if keyword.lower() in summarized_text.lower():
        filtered_keys.append(keyword)
        
print (filtered_keys)

Original text keywords: ['egyptians', 'nile river', 'egypt', 'nile', 'euphrates', 'tigris', 'old kingdom', 'red land', 'crown', 'upper', 'lower egypt', 'narmer', 'longest river', 'africa', 'mediterranean sea', 'hyksos', 'new kingdom', 'black land', 'ethiopia', 'middle kingdom']
Keywords present in summarized text: ['egyptians', 'nile river', 'egypt', 'nile', 'old kingdom', 'red land', 'crown', 'upper', 'lower egypt', 'narmer', 'africa', 'mediterranean sea', 'new kingdom', 'middle kingdom']

from nltk.tokenize import sent_tokenize
from flashtext import KeywordProcessor
def tokenize_sentences(text):
    sentences = [sent_tokenize(text)]
    sentences = [y for x in sentences for y in x]
    # Remove any short sentences less than 20 letters.
    sentences = [sentence.strip() for sentence in sentences if len(sentence) > 20]
    return sentences
def get_sentences_for_keyword(keywords, sentences):
    keyword_processor = KeywordProcessor()
    keyword_sentences = {}
    for word in keywords:
        keyword_sentences[word] = []
        keyword_processor.add_keyword(word)
    for sentence in sentences:
        keywords_found = keyword_processor.extract_keywords(sentence)
        for key in keywords_found:
            keyword_sentences[key].append(sentence)
for key in keyword_sentences.keys():
        values = keyword_sentences[key]
        values = sorted(values, key=len, reverse=True)
        keyword_sentences[key] = values
    return keyword_sentences
sentences = tokenize_sentences(summarized_text)
keyword_sentence_mapping = get_sentences_for_keyword(filtered_keys, sentences)
        
print (keyword_sentence_mapping)

{'egyptians': ['The Nile provided so well for Egyptians that sometimes they had surpluses, or more goods than they needed.', 'For example, some ancient Egyptians learned to be scribes, people whose job was to write and keep records.', 'Egyptians believed that if a tomb was robbed, the person buried there could not have a happy afterlife.', 
'nile river': ['The Nile River fed Egyptian civilization for hundreds of years.'], 
 'old kingdom': ['Historians divide ancient Egyptian dynasties into the Old Kingdom, the Middle Kingdom, and the New Kingdom.', 'The Old Kingdom started about 2575 B.C., when the Egyptian empire was gaining strength.'], }        .....................

Eg: Historians divide ancient __________  dynasties into the Old Kingdom, the Middle Kingdom, and the New Kingdom
a)Egyptian
b)Ethiopian
c)Angolian
d)Algerian

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

#############################################################################
NOTE::::::::  Since the algorithm might have errors along the way, wrong answer choices generated might not be correct for some questions. 
#############################################################################


1) The Nile provided so well for  _______  that sometimes they had surpluses, or more goods than they needed.
	 a )   Angolan
	 b )   Algerian
	 c )   Egyptians
	 d )   Bantu

More options:  ['Basotho', 'Beninese', 'Berber', 'Black African', 'Burundian', 'Cameroonian', 'Carthaginian', 'Chadian', 'Chewa', 'Congolese', 'Djiboutian', 'Egyptian', 'Ethiopian', 'Eurafrican', 'Ewe', 'Fulani'] 


2) As in many ancient societies, much of the knowledge of  _______  came about as priests studied the world to find ways to please the gods.
	 a )   Malawi
	 b )   East Africa
	 c )   Somalia
	 d )   Egypt

More options:  ['Togo', 'Zimbabwe', 'Gabon', 'Ghana', 'Lake Tanganyika', 'Ottoman Empire', 'Mozambique', 'Iran', 'Israel', 'Saudi Arabia', 'Lebanon', 'Turkey', 'Iraq', 'Levant', 'Syria', 'Jordan'] 


3) The  _______  provided so well for Egyptians that sometimes they had surpluses, or more goods than they needed.
	 a )   Nyala
	 b )   Omdurman
	 c )   Nile
	 d )   Port Sudan

More options:  ['Khartoum', 'Nubian Desert', 'Darfur', 'Libyan Desert', 'Kordofan', 'Gulu', 'Buganda', 'Entebbe', 'Jinja', 'Lake Edward', 'entebbe', 'gulu', 'kayunga', 'Upper Egypt', 'Suez Canal', 'Aswan High Dam'] 


4) It combined the red  _______  of Lower Egypt with the white  _______  of Upper Egypt.
	 a )   Capital
	 b )   Crown
	 c )   Masthead
	 d )   Head

More options:  [] 


5) It combined the red Crown of Lower Egypt with the white Crown of  _______  Egypt.
	 a )   Upper Berth
	 b )   Lower Berth
	 c )   Upper

More options:  []