Question Guru: An Automated Multiple-Choice Question Generation System

In the past few years, there has been great technological advancement in the field of deep learning and natural language processing. One of the applications is automatic generation of quizzes from text. The recent advancement in NLP techniques has shown a lot of promise. The proposed solution uses an NLP pipeline involving Bert and T5 transformers to extract keywords and gain insights from the text input. From the extracted keywords, different types of questions are generated such as fill in the blanks, true or false, Wh-type and multiple choice questions. Latest state-of-the-art models proved to perform better in all stages of our pipeline. The results from these models have shown a lot of promise. Through a survey created for evaluating the model, around 60% questions generated by the model were incorrectly identified as human generated or could not be determined by the survey participants.KeywordsDeep learningNatural language processingNLP pipelineQuiz generationState-of-the-art

Suicide remains a global public health crisis, with millions expressing emotional distress and suicidal ideation on social media platforms, often without receiving timely intervention. This study explores the potential of Natural Language Processing (NLP) to detect suicide-related content by leveraging deep learning models trained on real-time user-generated text. Harnessing the power of language, the project aims to develop and benchmark cutting-edge NLP architectures capable of identifying suicidal ideation with high accuracy, precision, and recall. Unlike traditional clinical assessments, social media provides raw, unfiltered insight into individuals' mental states, offering a critical opportunity for early detection and preventive action. The research evaluates multiple deep learning models including Bidirectional Encoder Representations from Transformers (BERT), Long Short-Term Memory (LSTM) networks, and hybrid CNN-LSTM architectures using labeled datasets from platforms such as Twitter and Reddit. Preprocessing techniques like tokenization, lemmatization, and noise reduction are applied to enhance semantic understanding. Furthermore, the study incorporates sentiment analysis, keyword extraction, and contextual embeddings to capture nuanced expressions of distress and intent. Performance is benchmarked across diverse metrics, including F1 score, Area Under the Curve (AUC), and Matthews Correlation Coefficient (MCC), with interpretability frameworks like LIME and SHAP used to ensure transparency in model predictions. The results demonstrate that contextual deep learning models significantly outperform traditional machine learning methods in detecting suicidal language patterns, even when disguised in metaphor, sarcasm, or indirect references. The study concludes by proposing an integrated framework for deploying these models in collaboration with mental health organizations, platform moderators, and emergency response services. Ethical considerations including user privacy, data anonymization, and false positive mitigation are emphasized to ensure responsible deployment. By bridging the gap between technology and mental health, this research underscores the transformative role of NLP in suicide prevention and advocates for AI-powered systems as a vital tool in safeguarding vulnerable populations in the digital age.

Assessment plays an important role in learning and Multiple Choice Questions (MCQs) are quite popular in large-scale evaluations. Technology enabled learning necessitates a smart assessment. Therefore, automatic MCQ generation became increasingly popular in the last two decades. Despite a large amount of research effort, system generated MCQs are not useful in real educational applications. This is because of the inability to produce the diverse and human alike distractors. Distractors are the wrong choices given along with the correct answer (key) to confuse the examinee. Especially, in educational domain (grammar learning) the MCQs deal with affix-based or morphologically transformed distractors. In this paper, we present a method for automatic generation of affix-based distractors for fill-in-the-blanks for learning Tamil Vocabulary. Affix-based distractor generation relies on certain regularities manifest in high dimensional spaces. We investigate the quality of distractors generated by a number of criteria, including Part-Of-Speech, difficulty level, spelling, word co-occurrence, semantic similarity and affixation. We evaluated our proposed method in grammar based Multiple Choice Questions (MCQs) dataset. The result shows that affix-based distractors, yield significantly more plausible outcomes in certain grammar based questions.

Multiple Choice Questions (MCQs) feature more and more in modern-day assessments due to more straightforward exams and faster checking. However, with this comes the challenge of creating a diverse pool of MCQs specific to a given subject matter. This work proposes an MCQ generation system that uses linguistic features and Deep Learning techniques to create MCQs from a given text. The entire process has three steps: i) The pre-trained DL state-of-the-art model summarizes a text paragraph to get relevant information. ii) Linguistic features generate question (stem) and answer (key) pairs. iii) Distractor generation using the key or correct answer. DL-based paraphrasing models are used to augment the MCQs dataset with questions of similar type and difficulty level.

Natural language processing (NLP) is an important and challenging field of computer science. With the advent of computers and related technologies, NLP has grown out of the field of linguistics and is getting more attention in the computer field to support a wide variety of intelligent applications. NLP basically refers to automatic processing of natural languages by computers or machines in order to understand human language in written or spoken form. Ontologies are semantic models that provide general description of the types and structures of objects in a particular subject area or domain. Core components of ontology include concepts, relationships, instances, and axioms. Ontology is used to depict the hierarchical organization of concepts and their relationships. Knowledge graphs represent a set of interconnected descriptions of objects or concepts, and are sometimes referred to as semantic networks. In short, an ontology can be treated as a meta-model for data and knowledge graphs as a representation of the actual data or facts. An ontology (a formal description of relationships) plays a vital role in building a knowledge graph. Ontology and knowledge graphs have proven to be powerful tools to manage enterprise or big data. Today, a number of applications rely on ontology and knowledge graphs, such as knowledge management, information discovery, text extraction, semantic search, content-based recommendation, and intelligent chatbots. This chapter focuses on the importance of ontology and knowledge graphs for Developing NLP-based applications is difficult because computers require structured data, and human speech is highly complex, extremely diverse, unstructured in nature, and often ambiguous. In general, manual creation of knowledge graphs is expensive, therefore any automation of the process is highly desirable. Here, ontologies and knowledge graphs play a crucial role by providing a well-structured knowledge base, allowing semi-automatic creation of a lexicon to be used effectively in language translations, spellcheckers, keyword extraction and similar NLP applications.

Keyword extraction is a critical technique in natural language processing. For this essential task we present a simple yet efficient architecture involving character-level convolutional neural tensor networks. The proposed architecture learns the relations between a document and each word within the document and treats keyword extraction as a supervised binary classification problem. In contrast to traditional supervised approaches, our model learns the distributional vector representations for both documents and words, which directly embeds semantic information and background knowledge without the need for handcrafted features. Most importantly, we model semantics down to the character level to capture morphological information about words, which although ignored in related literature effectively mitigates the unknown word problem in supervised learning approaches for keyword extraction. In the experiments, we compare the proposed model with several state-of-the-art supervised and unsupervised approaches for keyword extraction. Experiments conducted on two datasets attest the effectiveness of the proposed deep learning framework in significantly outperforming several baseline methods.

With the increasing influence of online media, company websites have become important communication channels between companies and customers. Companies use their websites as a marketing tool for a variety of purposes, including enhancing their image and selling products or services. Many researchers have examined the criteria, methods, and tools for website evaluation, but most have focused on usability. Prior content analyses have focused not on text content but on website components, an approach likely to produce subjective evaluations. This study attempts to objectively evaluate company websites by utilizing text mining. We analyze the usefulness of company websites by presenting visualized outputs from a business perspective, allowing practitioners to easily understand the results of the website evaluation and use them in decision making. To demonstrate our method empirically, we selected a company with a number of affiliates in Korea and analyzed the text content of their websites to assess their usefulness using natural language processing and graphics packages in R. Practitioners can easily employ our objective evaluation method, and researchers can use it to gain a new perspective on website evaluation.

Keywords at the beginning of research articles are crucial for conveying the content and main ideas of academic works. They serve as essential tools for researchers to efficiently search for relevant topics. The integration of traditional natural language processing (NLP) techniques with modern deep learning methods has significantly advanced keyword extraction across various languages. However, extracting keywords from Arabic research poses unique challenges due to the language's complex morphology, rich semantics, and a scarcity of available resources. This study aims at identifying and analyzing keywords in a sample of studies from 480 peer-reviewed Arabic journals across diverse scientific fields, leading to the development of a specialized dataset for Arabic keyword extraction. The dataset comprises 38,728 records, each containing abstract and authors-specified keywords. We utilized this novel dataset to train and evaluate several BERT-based models tailored for the Arabic language, including bertBaseArabic, bertBaseQarib, camelBERT, multidialectBERT, and baseAraBERT. Prior to training, the dataset underwent comprehensive pre-processing, including data cleaning, lemmatization, and binary tagging. The keyword extraction task was transformed into a binary classification problem, where tokens were labeled as either keywords or non-keywords, simplifying the learning process. Experimental results indicate that the bertBaseQarib model achieved the highest performance, with an F1-score of 0.951, precision of 0.968, and recall of 0.940. This study highlights the effectiveness of BERT-based models in Arabic keyword extraction and emphasizes the importance of large, diverse datasets in achieving robust NLP outcomes. Future work will focus on expanding the dataset and optimizing model architectures for even better performance.

In the era of digitalization, electronic gadgets such as Google Translate, Siri, and Alexa have at least one characteristic: They are all the products of natural language processing (NLP). “Natural Language” refers to a human language used for daily communication, such as English, Hindi, Bengali, etc. Natural languages, as opposed to artificial languages such as computer languages and mathematical nomenclature, have evolved as they have been transmitted from generation to generation and are challenging to explain with clear limits in the first instance. In natural language processing, artificial intelligence (Singh et al., 2021), linguistics, information processing, and cognitive science are all related fields (NLP). NLP aims to use intelligent computer techniques to process human language. However, NLP technologies such as voice recognition, language comprehension, and machine translation exist. With such limited obvious exclusions, machine learning algorithms in NLP sometimes lacked sufficient capacity to consume massive amounts of training data. In addition, the algorithms, techniques, and infrastructural facilities lack enough strength. Humans design features in traditional machine learning, and feature engineering is a limitation that requires significant human expertise. Simultaneously, the accompanying superficial algorithms lack depiction capability and, as a result, the ability to generate layers of duplicatable concepts that would naturally separate intricate aspects in forming visible linguistic data. Deep learning overcomes the challenges mentioned earlier by using deep, layered modelling architectures, often using neural networks and the corresponding full-stack learning methods. Deep learning has recently enhanced natural language processing by using artificial neural networks based on biological brain systems and Backpropagation. Deep learning approaches that use several processing layers to develop hierarchy data representations have produced cutting-edge results in various areas. This chapter introduces natural language processing (NLP) as an AI component. The history of NLP is next. Distributed language representations are the core of NLP's profound learning revolution. After the survey, the boundaries of deep learning for NLP are investigated. The paper proposes five NLP scientific fields.

ObjectivesTo assess the feasibility of using a natural language processing (NLP) application for extraction of free-text online activity mentions in adolescent mental health patient electronic health records (EHRs).SettingThe Clinical Records...

Sentiment analysis is a crucial part of natural language processing (NLP) that categorizes text into positive, negative, or neutral sentiments. This technology helps businesses automatically interpret customer emotions, enabling informed decisions in marketing, product development, and customer support. Traditional machine learning models like Random Forest, Naive Bayes, and Support Vector Machine (SVM) have been commonly used for sentiment classification, especially effective for short and concise texts such as brief customer comments. SVM, in particular, performs well in classifying sentiments in short texts. However, the emergence of advanced large language models (LLMs) like GPT-4 has revolutionized sentiment analysis by capturing more nuanced emotional expressions. GPT-4 excels in understanding complex context, sarcasm, and subtle emotional tones within longer texts, offering multi-dimensional sentiment insights such as satisfaction, frustration, and uncertainty. This makes it more capable than traditional models when analyzing detailed customer reviews or conversations. Comparative studies reveal that while traditional models handle short texts efficiently, GPT-4 surpasses them in analyzing in-depth content with greater precision, recall, and F1 scores. GPT-4 also uniquely identifies mixed sentiments that simpler models often miss. In conclusion, although traditional machine learning approaches remain useful for straightforward sentiment analysis tasks, GPT-4 provides a more sophisticated and comprehensive understanding of customer emotions in complex texts. Integrating GPT-4 into sentiment analysis workflows can significantly enhance the accuracy and richness of insights derived from customer feedback, supporting better business decisions.

In thee-commerce landscape,personalized recommendation significantly enhance customer satisfaction and engagement. This project presents the development of a recommendation system that utilizes Artificial Intelligence (AI) and Natural Language Processing (NLP) to analyze customer reviews for better product or service suggestions. The system processes large volumes of textual data using sentiment analysis, keyword extraction, and topic modeling to identify preferences, needs, and trends. It integrates collaborative and content-based filtering methods to offer relevant and adaptive recommendations based on user feedback. The resulting system demonstrates improved accuracy, user trust, and scalability for real-world applications. The recommendation engine combines collaborative filtering, which analyzes user behavior, with content-based filtering, which considers the product attributes mentioned in reviews. By integrating both methods, the system provides personalized and highly relevant recommendations tailored to individual user profiles. It adapts to evolving preferences by continuously learning from new data and user feedback. This approach ensures that the system not only predicts what users might like but also anticipates needs based on past interactions and review data. The result is an intelligent, scalable system that improves over time and provides an enhanced user experience.

Keyword extraction is an important task in natural language processing, supporting summarization, indexing, content analysis, and information retrieval. Over time, a wide range of methods have been developed to automate the identification of key concepts in text. This study reviews the main families of keyword extraction techniques, including statistical methods, graph-based algorithms, embedding-based models, and approaches that use neural representations. In addition to summarizing existing research, the study evaluates several widely used tools, such as TF-IDF, YAKE, KeyBERT, KPMiner, TextRank, TopicRank, and MultipartiteRank, and examines how their strengths and limitations appear when applied to real educational materials. Special attention is given to the challenges that arise in multilingual and low-resource settings, particularly for languages with fewer annotated resources. The analysis highlights patterns in algorithm behavior, differences in semantic coverage, and factors that influence performance across syllabus formats and subject areas. The findings aim to help researchers and practitioners choose appropriate keyword extraction methods for educational, multilingual, and domain-specific applications.

Keyword extraction is the task of identifying important terms or phrase that are most representative of the sourcedocument. Although the process of automatic extraction of keywords from title is an old method, it was mainly forextraction from a single web document. Our approach differs from previous research works on keyword extraction in severalaspects. For those who are non-expert of the scientific fields, understating scientific research trends is difficult. The purposeof this study is to develop an automatic method of obtaining overviews of a scientific field for non-experts by capturingresearch trends. This empirical study excavates significant term extraction using Natural Language Processing (NLP) tools.More than 15000 titles saved in a .csv file was our dataset and scripts written in Python were our process to compare how farsignificant terms of scientific title corpus are similar or different to the terms available in the abstract of that same scientificarticle corpus. A light-weight unsupervised title extractor, Yet Another Keyword Extractor (YAKE) was used to extract theresults. Based on our analysis, it can be concluded that these algorithms can be used for other fields too by the non-expertsof that subject field to perform automatic extraction of significant words and understanding trends. Our algorithm could be asolution to reduce the labour-intensive manual indexing process.

Document classification plays an important role in natural language processing. Among that, keyword extraction algorithm shows its great potential in summarizing the entire document. Attention is the process of selectively concentrating on a discrete aspect of information, while ignoring other perceivable information. A new probabilistic keyword extraction algorithm is proposed, which is inspired by the visual attention mechanism. An unsupervised neural network based pre-training method is proposed for training the semantic attention based keyword extraction algorithm, which is helpful in extracting keywords with rich contextual information from the document. A bidirectional Long short-term memory network combined with the proposed semantic keyword extraction algorithm is designed for both topic and sentiment classification tasks. Experiments on four large scale datasets show that the proposed visual attention based keyword extraction algorithm gives a better performance than the baseline methods. The semantic attention based keyword extraction method is significant in summarizing the content of a document, which is very useful for large scale document classification.