(self, q_id,question, args)
Retrieve articles with their corresponding hyperlinked titles.
        Due to efficiency, we sample top k articles, and then sample top l paragraphs from each article. 
        (so, eventually we get k*l paragraphs with tfidf-based pruning.)
        We also store the hyperlinked titles for each paragraph.
Retrieve articles with their corresponding hyperlinked titles.
        Due to efficiency, we sample top k articles, and then sample top l paragraphs from each article. 
        (so, eventually we get k*l paragraphs with tfidf-based pruning.)
        We also store the hyperlinked titles for each paragraph.
Retrieve articles with their corresponding hyperlinked titles . Due to efficiency we sample top k articles and then sample top l paragraphs from each article . ( so eventually we get k * l paragraphs with tfidf - based pruning . ) We also store the hyperlinked titles for each paragraph .
def get_article_tfidf_with_hyperlinked_titles(self, q_id,question, args):
        """
        Retrieve articles with their corresponding hyperlinked titles.
        Due to efficiency, we sample top k articles, and then sample top l paragraphs from each article. 
        (so, eventually we get k*l paragraphs with tfidf-based pruning.)
        We also store the hyperlinked titles for each paragraph. 
        """

        tfidf_limit, pruning_l, prune_after_agg = args.tfidf_limit, args.pruning_l, args.prune_after_agg
        doc_names, _ = self.ranker.closest_docs(question, k=tfidf_limit)
        context, hyper_linked_titles = self.load_sampled_para_text_and_linked_titles(
            doc_names, question, pruning_l, prune_after_agg)
        
        if args.tagme is True and args.tagme_api_key is not None:
            # if add TagMe
            tagged_context = self.load_sampled_tagged_para_text(
                question, pruning_l, args.tagme_api_key)
            
            return [{"question": question,
                     "context": context,
                     "tagged_context": tagged_context,
                     "all_linked_para_title_dic": hyper_linked_titles,
                     "q_id": q_id}]
        else:
            return [{"question": question,
                    "context": context,
                    "all_linked_para_title_dic": hyper_linked_titles,
                    "q_id": q_id}]
def get_article_tfidf_with_hyperlinked_titles ( self , q_id , question , args ) : tfidf_limit , pruning_l , prune_after_agg = args . tfidf_limit , args . pruning_l , args . prune_after_agg doc_names , _ = self . ranker . closest_docs ( question , k = tfidf_limit ) context , hyper_linked_titles = self . load_sampled_para_text_and_linked_titles ( doc_names , question , pruning_l , prune_after_agg ) if args . tagme is True and args . tagme_api_key is not None : # if add TagMe tagged_context = self . load_sampled_tagged_para_text ( question , pruning_l , args . tagme_api_key ) return [ { "question" : question , "context" : context , "tagged_context" : tagged_context , "all_linked_para_title_dic" : hyper_linked_titles , "q_id" : q_id } ] else : return [ { "question" : question , "context" : context , "all_linked_para_title_dic" : hyper_linked_titles , "q_id" : q_id } ]