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Bidirectional Training for Generating Math Word Problems Using Pre-Trained Model and Prompt 20

Bidirectional Training for Generating Math Word Problems Usi...

引用

20th international Computer conference on Wavelet Active Media Technology and Information processing, ICCWAMTIP 2023

作者： Kai, Gao School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350318982

Math word problems (MWPs) are a vital type of exercise that greatly enhances students' mathematical proficiency and problem-solving abilities. This paper aims to explore the challenging task of automatically generating MWPs, which necessitates the integration of mathematical knowledge, natural language processing, and logical reasoning. We propose an innovative approach that utilizes bidirectional training and prompt construction to fine-tune pre-trained language models. We evaluate our framework on multiple datasets and metrics, demonstrating its effectiveness and superiority over baselines. Our framework introduces a novel tool and perspective for mathematical education and research, creating new possibilities for generating various types of MWPs. © 2023 IEEE.

关键词： Computational linguistics

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Are my Answers Medically Accurate? Answers Reliability Regarding Questions over Healthcare knowledge Graphs 21

Are my Answers Medically Accurate? Answers Reliability Regar...

引用

2024 IEEE/ACS 21st international conference on Computer Systems and Applications, AICCSA 2024

作者： Baazouzi, Wiem Kachroudi, Marouen Faiz, Sami National School of Computer Science University of Manouba RIADI Laboratory Manouba2010 Tunisia ESPRIT School of Engineering El Ghazala2080 Tunisia Université de Tunis El Manar Faculté des Sciences de Tunis Informatique Programmation Algorithmique et Heuristique LR11ES14 Tunis2092 Tunisia Université de Tunis El Manar Ecole Nationale d'Ingénieurs de Tunis Laboratoire de Teledetection et Systemes d'Information A Reference Spatiale 99/UR/11-11 Tunis2092 Tunisia

ISBN: (纸本)9798331518240

In recent years, there has been a growing interest in the digitalization of medical records and health data. This makes it possible to build sophisticated systems that facilitate rapid data retrieval, processing and analysis. An example of such systems is Question-Answer ones. It can help domain experts efficiently retrieve the required data by asking questions in natural language. Question-Answering systems based on knowledge Graphs have found applications in various fields. Our work aims to build a question-Answering chatbot for electronic medical records. Thus, healthcare professionals can ask questions in natural language, and the chatbot will iteratively build and execute appropriate SPARQL queries over a given knowledge Graph containing digital medical records to return relevant data. © 2024 IEEE.

关键词： Electronic health record

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Assessing the Ability of AI-Driven natural language processing to Accurately Analyze Unstructured Text Data 3

Assessing the Ability of AI-Driven Natural Language Processi...

引用

3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Vigenesh, M. RaviKumar, V. Patil, Vivek D. Kumar, Sharma Sonu Geyi, Nyarik Chattopadhyay, Subir Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Mangalayatan University Department of Computer Science Aligarh India Himalayan University Department of Bussiness & Management Itanagar India Usha Martin University Faculty of Business Mangagement & Commerce Ranchi India

ISBN: (纸本)9798350319125

This technical abstract examines the ability of herbal language processing (NLP)-driven synthetic Intelligence (AI) to analyze unstructured textual content facts. Unstructured textual content records include information from assets with webpages, social media, emails, and consumer comments. They are widely used in sentiment evaluation, hazard detection, and entity extraction programs. NLP-pushed AI answers along with natural language knowledge (NLU), herbal language knowledge (NLG), and natural language understanding (NLP) are used to manner and examine unstructured textual content information. The analysis technique starts evolving with tokenizing the facts, breaking the textual content into smaller factors, including phrases and phrases. After tokenizing the statistics, the AI device discovers styles inside the records. Those styles can then discover the emotions and entities inside the data. NLU and NLG models are used to expand the facts' expertise by assigning sentiment and issue-level scores. The accuracy of NLP-driven AI is similarly progressed through more excellent complex fashions consisting of deep studying and reinforcement. © 2023 IEEE.

关键词： extraction phrases statistics tokenized webpages

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Transforming international Student Admissions with Artificial Intelligence: Strategies and Outcomes

Transforming International Student Admissions with Artificia...

引用

2024 international conference on knowledge engineering and Communication Systems, ICKECS 2024

作者： Amruta Raman, Ramakrishnan India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359688

The integration of Artificial Intelligence (AI) into the international student admissions process represents a paradigm shift, enabling institutions to navigate the complex landscape of global education with enhanced precision and efficiency. This paper delves into the transformative role of AI in streamlining admissions, from initial application screening to final selection, thereby fostering a more diverse, talented, and well-matched student body. Through a comprehensive analysis, we unveil AI-driven strategies such as predictive analytics for applicant success, natural language processing for personal statement evaluation, and machine learning algorithms for holistic profile assessment. The outcomes of these AI interventions are critically evaluated through a series of case studies, demonstrating significant improvements in operational efficiency, decision-making accuracy, and applicant satisfaction. Furthermore, our research highlights the ethical considerations and challenges encountered in the AI-driven admissions process, proposing solutions to ensure fairness, transparency, and inclusivity. The findings underscore the potential of AI to revolutionize international student admissions, offering insights into best practices and future directions for educational institutions worldwide. By harnessing the power of AI, these institutions can not only optimize their admissions processes but also contribute to a more accessible and equitable global education landscape. © 2024 IEEE.

关键词： Predictive analytics

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knowledge Weightage Calculation: an AI & ML based smart modelling for text- content summarization and quantification 1

Knowledge Weightage Calculation: an AI & ML based smart mode...

引用

1st international conference on Intelligent Computing and Research Trends, ICRT 2023

作者： Kumar, Aman Rizvi, Danish Raza Jamia Millia Islamia Faculty of Engineering Dept. of Computer Engineering New Delhi India

ISBN: (纸本)9798350336771

The need for naturalistic interaction and communication between humans and machines is essential for high-level artificial intelligence. Matured natural language processing is the first step towards attaining artificial intelligence by automation equal to human beings. It has been a hot and active area of research among researchers for a long. In the advanced forms of natural language processing-based intelligent application building must-have capabilities of generating summaries, understanding context, representing and combining multiple information, and knowledge extraction. Artificial Intelligence and various computing ways (Statistical Reasoning, Machine Learning, and Deep Learning) for achieving it, have been proven the next big weapon for attaining a true sense of intelligent automation and smart system building. Many tasks related to natural language processing have shown consistent and comparable high performances since the introduction of deep learning modelling. Using sequence-to-sequence models has been advantageous and demonstrated promising outcomes in many other areas, including machine translation, speech recognition, picture captioning, etc. This paper aims to propose a novel framework to extract important features from an unstructured text by removing the noisy, irrelevant, and redundant components, and to provide accountability for the information delivered by retrieving image-based text, deep text summarization by knowledge weight calculation, and representation- based on the extracted features. © 2023 IEEE.

关键词： Speech recognition

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Large language Models for Indian Legal Text Summarisation 10

Large Language Models for Indian Legal Text Summarisation

引用

10th IEEE international conference on Electronics, Computing and Communication Technologies (IEEE CONECCT)

作者： Hemanth, Kumar M. Jayanth, P. Anand, Kumar M. Natl Inst Technol Karnataka Dept Informat Technol Artificial Intelligence Surathkal India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939;9798350385922

Summarizing legal case judgments is a complex task in Legal natural language processing (NLP), with a gap in understanding how various summarization models, including extractive and abstractive approaches and analysing the perform within the domain of legal documents. Since there are around 4 crore pending cases in the Indian court system, this study addresses the challenge of laborious task of manually summarizing legal documents. It introduces both supervised and unsupervised models for both extractive and abstractive summarization, showcasing their effective performance through evaluations using ROUGE metrics and BERT score. BART, T5, PEGASUS, ROBERTA, Legal-PEGASUS, Legal-BERT models are used for abstractive summarisation. TextRank, LexRank, LSA, Summarizer BERT, KL-Summ are used in case of extractive summarisation. Longformer, Bert - Legal Pegasus are also considered for the task of Summarisation. In the domain of legal document summarization, we used GPT-4 and LLAMA-2, employing prompt engineering with both Zero-shot and One-shot prompts to extract summaries. As far of our knowledge, this is the first paper that used Large language Models like GPT-4 and LLama-2 for the task of Legal Text summarisation. Along with that a user-friendly chatbot has been developed utilizing the Llama model and specifically designed to respond for queries related to legal texts. Additionally, a web application has been created, allowing users to upload legal documents for summarization. An option is given to users to select from various languages including Telugu, Tamil, Kannada, Malayalam, and Hindi. As a result the summarised text is converted into respective language.

关键词： Legal,Summarisation NLP Large language models GPT-4 LLAMA-2

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Approaches towards Fake News Detection using Machine Learning and Deep Learning 10

Approaches towards Fake News Detection using Machine Learnin...

引用

10th international conference on Signal processing and Integrated Networks, SPIN 2023

作者： Kumar, Nitish Kar, Nirmalya National Institute of Technology Agartala Department of Computer Science and Engineering India

ISBN: (纸本)9781665490993

Fake news evolving around us for a very long time. The gradual growth of social media platforms has provided us with an easily accessible and publishable news platform in front of the audience that news may be true or False. The spreading of fake news increased as compared to ancient times. Nowadays Fake news detection become a tough challenge for Both natural language processing(NLP) and Machine Learning (ML) experts. For detecting fake news fact-checking is also very important. In this paper, we focus on the analysis of recently published papers in this domain and the analysis of different techniques for detecting fake news. Through this survey, we will get inside knowledge of the detection process of fake news using different natural language processing, machine learning, and Deep Learning Techniques. © 2023 IEEE.

关键词： Support vector machines

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Enhancing Hindi Named Entity Recognition through Ensemble Learning 10

Enhancing Hindi Named Entity Recognition through Ensemble Le...

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10th IEEE Uttar Pradesh Section international conference on Electrical, Electronics and Computer engineering, UPCON 2023

作者： Singh, Aayushi Singh, Sumit Tiwary, Uma Shanker Iiit Allahabad Information Technology Prayagraj India

ISBN: (纸本)9798350382471

The main goal of the NER task, a well-known research field, is to identify people, groups, places, and other business entities in text. This task entails classifying and recognising these named things, which is essential for many applications of natural language processing. In this study, we used ensemble approaches to improve the NER task performance. We enhanced outcomes in terms of accuracy, precision, and recall by merging numerous models and their predictions. The ensemble approach offered a stronger and more dependable solution for named entity recognition by successfully addressing the shortcomings of individual models. The outcomes of the experiment showed how the ensemble technique could improve the NER task's overall performance. © 2023 IEEE.

关键词： Cross entropy Ensemble learning knowledge-base Named Entity Recognition natural language processing Transformer

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Proceedings of 2024 international conference on Machine Intelligence and Digital Applications, MIDA 2024

Proceedings of 2024 International Conference on Machine Inte...

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2024 international conference on Machine Intelligence and Digital Applications, MIDA 2024

ISBN: (纸本)9798400718144

The proceedings contain 136 papers. The topics discussed include: financial engineering and computer science: application of deep learning in the field of big data finance: taking stock forecasting analysis for example;exploration of distributed power transaction mechanism based on blockchain;cohesive group queries of collective spatial keywords based on road network;power grid data model based on IFC and its application in the whole process management;research on Russian cultural transliteration algorithm based on Hidden Markov model;research on computer natural language processing intelligent question answering system based on knowledge graph;three-dimensional reconstruction of tomato fruit based on multi-view images;and research on real-time processing and stream analysis of unstructured data based on big data platforms.

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Automatic Question Generation from Youtube Lectures using Deep Learning 15

Automatic Question Generation from Youtube Lectures using De...

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15th international conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Jasuja, Himanshu Negi, Ujjwal Vibhav Kaur, Gull Delhi Technological University Department of Computer Engineering New Delhi India

ISBN: (纸本)9798350370249

In this contemporary world full of information, online lecture videos are a big fountain of knowledge. Nevertheless, quizzes have to be developed based on these videos to make evaluation of knowledge acquisition much easier. The research describes a method for generating quizzes from online teaching videos that enhances self-learning through continuous assessment. Unlike existing approaches which are resource intensive and computationally demanding, we aim at providing a Video Question Generation model that is light weight and effective. We take advantage of state-of-the-art natural language processing (NLP) technology to improve our model's flexibility and allow it to be fine-tuned using T5 transformers. Our system also generates various forms of 'Wh' questions such as who, when, where, what, which, why and how as well as Multiple Choice Questions (MCQs). Through this study we hope to give teachers and students alike a tool that can facilitate knowledge assessment and create an active learning environment. © 2024 IEEE.

关键词： knowledge acquisition

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