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LSTM Sequence to Sequence Model for Dynamic Title Generation 7

LSTM Sequence to Sequence Model for Dynamic Title Generation

引用

7th international conference on Inventive Computation Technologies, ICICT 2024

作者： Shilaskar, Swati Saitwal, Prerna Raundal, Pranav Rathi, Manas Vishwakarma Institute of Technology Dept. of Electronics & Telecommunication Engineering Pune India

ISBN: (纸本)9798350359299

To make research communication more efficient, this research work introduces a smart way to create titles for research papers automatically from their corresponding titles. Leveraging a diverse dataset of research papers, the study employs a comprehensive pipeline involving data preprocessing, neural network modeling, and evaluation. Our methodology employs advanced natural language processing techniques, including LSTM-based sequence-to-sequence models and word embeddings, to capture the relationship between abstracts and titles. The trained model is rigorously optimized, and its performance is evaluated using established metrics. An accuracy of 78.82% and perplexity of 1.005 is achieved. This means that the model is able to generate titles that were very similar to the human-generated titles. The results demonstrate the feasibility of automated title generation, providing titles that closely resemble human-generated counterparts. This research offers valuable insights into enhancing scholarly publishing and knowledge dissemination across academic domains. © 2024 ieee.

关键词： natural language processing systems

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Legal Assistance Redefined: Transforming Legal Access with AI-Powered LegalLink

Legal Assistance Redefined: Transforming Legal Access with A...

引用

2024 ieee international conference on Signal processing, Informatics, Communication and Energy Systems, SPICES 2024

作者： Jacob, Susan Jacob, Prisly Mary Cheriyan, Jo Ismail, Safad Saintgits College of Engineering Department of Computer Science and Engineering Kerala Kottayam India

ISBN: (纸本)9798350376135

In today's scenario, securing prompt and accurate legal aid remains tough for many due to India's complex legal system, a scarcity of lawyers, high fees, and widespread legal unawareness. To address these issues, we plan to create a comprehensive chatbot designed to provide easy access to valuable legal information for the general public. The LegalLink chatbot leverages the Nomic AI text encoder for encoding user queries, followed by the Retrieval-Augmented Generation (RAG) model for retrieving and integrating external knowledge sources into prompt templates. Additionally, it utilizes the Mixtral-8x7B-Instruct-v0.1 model for generating contextually rich and accurate responses tailored to legal inquiries. This paper outlines the chatbot's architecture, various ML models used, and the methodology adopted. Leveraging cutting-edge artificial intelligence, LegalLink revolutionizes the way people access legal guidance, offering timely, accurate, and user-friendly assistance. © 2024 ieee.

关键词： natural language processing systems

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Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large language Models 25

Optimization Strategies for Enhancing Resource Efficiency in...

引用

2025 international conference on Performance engineering-ICPE-Annual

作者： Wallace, Tom Ombuki-Berman, Beatrice Ezzati-Jivan, Naser Brock Univ St Catharines ON Canada

ISBN: (纸本)9798400710735

Advancements in natural language processing are heavily reliant on Transformer architectures, whose improvements come at substantial resource costs due to ever-growing model sizes. This study explores optimization techniques, including quantization, knowledge distillation, and pruning, focusing on energy and computational efficiency while retaining performance. Among standalone methods, 4-Bit quantization significantly reduces energy use with minimal accuracy loss. Hybrid approaches, like NVIDIA's Minitron approach combining KD and structured pruning, further demonstrate promising trade-offs between size reduction and accuracy retention. A novel optimization framework is introduced, offering a flexible framework for comparing various methods. Through the investigation of these compression methods, we provide valuable insights for developing more sustainable and efficient LLMs, shining a light on the often-ignored concern of energy efficiency.

关键词： natural language processing Energy Efficiency Time Efficiency Model Compression

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Research on Named Entity Recognition Algorithm Based on NLP 12

Research on Named Entity Recognition Algorithm Based on NLP

引用

12th ieee international conference on Information and Communication Networks, ICICN 2024

作者： Qu, WangYue Li, JianLiang College of Information Engineering Northwest Agriculture and Forestry University Shaanxi Yangling China

ISBN: (纸本)9798350355802

In recent years, with the rapid development of natural language processing technology, Named Entity Recognition (NER), as one of the key tasks in the field of NLP, has attracted wide attention. NER plays an important role in knowledge graph construction, machine translation, question answering system and other downstream applications. This paper aims to investigate named entity recognition algorithms based on deep learning, and propose an improved lattice model method (SLBERT) and a new method based on Machine Reading Comprehension (MRC) (GFMRC) to improve the performance of NER. The experimental results demonstrate that our proposed method is effective and achieves state-of-the-art performance compared to other methods. © 2024 ieee.

关键词： Adversarial machine learning

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Embedding Model for Low-Resource Carpathian Ruthenian language as Western Ukrainian Dialect Based on NLP and Machine Learning 17

Embedding Model for Low-Resource Carpathian Ruthenian Langua...

引用

17th ieee international conference on Advanced Trends in Radioelectronics, Telecommunications and Computer engineering, TCSET 2024

作者： Vysotska, Victoria Markiv, Oksana Vladov, Serhii Sokurenko, Valerii Chyrun, Lyubomyr Lytvynenko, Yuliia Lviv Polytechnic National University Information Systems and Networks Department Lviv Ukraine Kremenchuk Flight College of Kharkiv National University of Internal Affairs Department of Aircraft Maintenance Kremenchuk Ukraine Kharkiv National University of Internal Affairs Kharkiv Ukraine Ivan Franko National University of Lviv Applied Mathematics Department Lviv Ukraine

ISBN: (纸本)9798331520564

The article dwells upon the development of the embedding model for the low-resource language as a Western dialect of the Ukrainian language, in particular Carpathian Ruthenian, development of natural language processing technologies and provision of support for information systems in the region of residence of the relevant ethnic group. © 2024 ieee.

关键词： Adversarial machine learning

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Biomedical Lay Summarization: Research Progress and Challenges 3

Biomedical Lay Summarization: Research Progress and Challeng...

引用

3rd international conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Bhowmik, Souramita Jamatia, Anupam Rudrapal, Dwijen Chakma, Kunal National Institute of Technology Agartala Computer Science & Engineering Department Agartala India

ISBN: (纸本)9798331523893

Biomedical lay summarization focuses on transforming complex scientific concepts into clear, concise, and accessible language, avoiding specialized technical terminology. This approach enables audiences without specialized knowledge to understand intricate biomedical topics. As research in biomedical lay summarization progresses, it aims to deliver accurate and user-friendly health information. Lay summaries not only condense content but also simplify language, enhancing both accessibility and readability. This review provides an extensive analysis of the field, highlighting key methods, models, evaluation systems, and challenges. The study surveys recent advancements in summarization quality and underscores the potential of biomedical lay summarization to improve patient engagement, public health communication, and informed decision-making. This thorough review encourages further innovation and emphasizes the need for multimodal approaches in biomedical summarization. © 2025 ieee.

关键词： Biomedical Biomedical Lay summarization Lay-friendly language Machine learning natural language processing Summarization

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CogniDual Framework: Self-Training Large language Models within a Dual-System Theoretical Framework for Improving Cognitive Tasks

CogniDual Framework: Self-Training Large Language Models wit...

引用

2025 ieee international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Deng, Yongxin Qiu, Xihe Tan, Xiaoyu Qu, Chao Pan, Jing Cheng, Yuan Xu, Yinghui Chu, Wei School of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai China Co. Ltd. Shanghai China School of Art Design and Architecture Monash University Melbourne Australia Artificial Intelligence Innovation and Incubation Institute Fudan University Shanghai China

ISBN: (纸本)9798350368741

Cognitive psychology investigates perception, attention, memory, language, problem-solving, decision-making, and reasoning. Kahneman's dual-system theory elucidates the human decision-making process, distinguishing between the rapid, intuitive System 1 and the deliberative, rational System 2. Recent advancements have positioned large language Models (LLMs) as formidable tools nearing human-level proficiency in various cognitive tasks. Nonetheless, the presence of a dual-system framework analogous to human cognition in LLMs remains unexplored. This study introduces the CogniDual Framework for LLMs (CFLLMs), designed to assess whether LLMs can, through self-training, evolve from deliberate deduction to intuitive responses, thereby emulating the human process of acquiring and mastering new information. Our findings reveal the cognitive mechanisms behind LLMs' response generation, enhancing our understanding of their capabilities in cognitive psychology. Practically, self-trained models can provide faster responses to certain queries, reducing computational demands during inference. © 2025 ieee.

关键词： cognitive psychology language processing large language model

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If [YourName] Can Code, So Can You! End-User Robot Programming For Non-Experts 24

If [YourName] Can Code, So Can You! End-User Robot Programmi...

引用

19th Annual ACM/ieee international conference on Human-Robot Interaction (HRI)

作者： Swaminathan, Manasi Hsu, Long-Jing Thant, Min Min Amon, Kyrie Jig Kim, Anna S. Tsui, Katherine M. Sabanovic, Selma Crandall, David J. Khoo, Weslie Indiana Univ Bloomington IN 47405 USA

ISBN: (纸本)9798400703232

Social robots are being studied for a wide variety of user populations, such as older adults, but programming these social robots typically requires deep technical knowledge. In this study, we developed a no-code end-user robot programming interface, with the goal of our interface being to empower individuals with no programming background to easily create social robot interactions with older adults using natural language. We evaluated five individuals with connections to adults older than 65 without robot programming experience. They were tasked with designing a simple conversation with the robot. We recorded their experiences using a survey and found that participants successfully used the interface to make the robot communicate with older adults. Overall, the participants found the interface easy to use and enjoyed the process. Thus, we provide recommendations on how to improve no-code end-user robot programming interfaces further.

关键词： human-centered interaction design conversational systems robotics machine learning natural language processing

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Enhancing Contextual Understanding in knowledge Graphs: Integration of Quantum natural language processing with Neo4j LLM knowledge Graph

Enhancing Contextual Understanding in Knowledge Graphs: Inte...

引用

2024 ieee international conference on Big Data, BigData 2024

作者： Bharti, Suman Lo, Dan Chia-Tien Shi, Yong Kennesaw State University Computer Science Department KennesawGA United States Kennesaw State University Department of Computer Science MariettaGA United States

ISBN: (纸本)9798350362480

Traditional knowledge Graphs (KGs), such as Neo4j, face challenges in managing high-dimensional relationships and capturing semantic nuances due to their deterministic nature. Quantum natural language processing (QNLP) introduces probabilistic reasoning into the KG context. This integration leverages quantum principles, such as superposition, which allows relationships to exist in multiple states simultaneously, and entanglement, where the state of one entity dynamically influences the state of another. This quantum-based probabilistic reasoning provides a richer, more flexible representation of connections, moving beyond binary relationships to model the nuances and variability of real-world interactions. Our research demonstrates that QNLP enhances Neo4j's ability to analyze context-rich data, improving tasks like entity extraction and knowledge inference. By modeling relationship states probabilistically, QNLP addresses limitations in traditional methods, providing nuanced insights and enabling more advanced, context-aware NLP applications. © 2024 ieee.

关键词： knowledge graph

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Leveraging Weak Supervision and BiGRU Neural Networks for Sentiment Analysis on Label-Free News Headlines 3

Leveraging Weak Supervision and BiGRU Neural Networks for Se...

引用

3rd ieee international conference on AI in Cybersecurity, ICAIC 2024

作者： Jamali, Ahamadali Alipour, Shahin Rah, Audrey Azad University Science and Research Branch Department of Computer Science Tehran Iran University of Houston Department of Biomedical Engineering TX United States University of Houston Department of Computer Engineering TX United States

ISBN: (纸本)9798350381856

Auto-labeling of text is a useful and necessary technique for creating large and high-quality training data sets for machine learning models. Label-free sentiment classification is a challenging semi-supervised task in the natural language processing domain. This study leveraged the weak supervision framework to generate weak labels in three categories for millions of news headlines from Australian Broadcasting Corporation (ABC). A Bidirectional Gate Recurrent Unit (BiGRU) was then trained with neural network dense layers to achieve a validation accuracy of 96.76% with 99.99% accuracy. The performance of this method was also compared with traditional and deep learning natural language processing techniques. © 2024 ieee.

关键词： Sentiment analysis

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