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Exploring language Models: A Comprehensive Survey and Analysis

Exploring Language Models: A Comprehensive Survey and Analys...

引用

Research Methodologies in knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), international conference on

作者： Aditi Singh Department of Computer Science Cleveland State University Ohio USA

The domain of natural language processing (NLP) has witnessed significant advancements with the arrival of large-scale pre-trained language models to revolutionize NLP research and achieve state-of-the-art performance across various tasks. They have propelled the development of sophisticated NLP applications and deepened our understanding of artificial intelligence. However, the growing size and complexity of these models have given rise to new challenges and limitations. Concerns related to model bias, interpretability, data privacy, and environmental impact have become prominent. This paper explores the impact and potential of large language models in NLP, highlighting the advancements made and the challenges that need to be addressed.

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Identifying Advantages of Convolutional Neural Networks for language Modeling and Understanding

Identifying Advantages of Convolutional Neural Networks for ...

引用

international conference on Computing and Networking Technology (ICCNT)

作者： Gopalakrishna V Gaonkar Shahin Fatima Swati Gupta Department of Civil Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of English ARKA JAIN University Jamshedpur Jharkhand India Department of Life Sciences Vivekananda Global University Jaipur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Convolutional neural networks (CNNs) are powerful machine learning models that have been used for a spread of programs, including image reputation, natural language processing, and language modeling and know-how. Similar to the traditional techniques for language modeling and information, CNNs can offer blessings over these techniques, along with reducing the number of parameters wished and enhancing performance on sequence files or lengthy documents. The advantage of CNNs to language modeling and understanding comes from their capacity to capture the interactions among parts of a sentence or phrase in a chain. This allows for the shooting of complicated members of the family among words in a sentence or a bigger textual content, decreasing the want for manual function engineering and the range of parameters wanted for accurate predictions. This is mainly crucial while running with sentence or file-degree statistics, wherein lengthy-time period phrase dependencies may additionally exist. Moreover, CNNs can offer better results with much fewer records in comparison to traditional techniques. They gain this result with the aid of leveraging the convolutional layer to discover capabilities among phrases or sentences, taking into account effective facts sharing and allowing CNNs to study greater quickly and correctly.

关键词： knowledge engineering Recurrent neural networks Accuracy Computational modeling Manuals Machine learning natural language processing Computational efficiency Convolutional neural networks

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Exploring natural language processing Model Acceleration in Molecular Dynamics Simulation Using High-Performance Computing and Machine Learning

Exploring Natural Language Processing Model Acceleration in ...

引用

ieee international conference on Bioinformatics and Biomedicine (BIBM)

作者： Qi Du Feng Wang Hui Huang Heng Wan Xiaoyu Wu Chengkun Wu College of Computer Science and Electronic Engineering Hunan University Changsha China Hunan Institute of Scientific and Technical Information Changsha China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

In molecular dynamics simulations, the integral methods used to calculate molecular trajectories, such as the Verlet integral method, involve significant computational costs. On the premise of ensuring the accuracy of simulation, how to effectively reduce the amount of computation is always a challenging problem. This paper applies natural language processing models to simulate molecular motion trajectories in molecular dynamics, integrating the MPI programming model and machine learning on a high-performance computing platform to achieve MPI parallelization during model training and inference. The results indicate that different types of neural network architectures have a significant impact on inference performance and accuracy. When using the deep spatio-temporal networks model, the mean absolute error is 0.0032. At an atomic scale of 3M, the parallel efficiency with 1024 computational nodes exceeds 90%, demonstrating excellent parallel performance.

关键词： Training Accuracy Computational modeling High performance computing Neural networks Machine learning Computer architecture natural language processing Trajectory Computational efficiency

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Public Healthcare Informatics for COVID-19 from Social Media Data

Public Healthcare Informatics for COVID-19 from Social Media...

引用

Annual international conference of the ieee engineering in Medicine and Biology Society (EMBC)

作者： Vishwa V. Kumar Avimanyu Sahoo Ritika Kumar Nicholas Loyd Industrial & Systems Engineering and Engineering Management Department of Electrical and Computer Engineering The University of Alabama in Huntsville USA North Alabama Medical Center Florence AL USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The COVID-19 pandemic has profoundly affected various facets of society. This research explores the discourse on social media, specifically "X" (Twitter), to understand public sentiment and needs regarding healthcare supply chain challenges, such as the availability of testing kits, oxygen supplies, and hospital beds, during the pandemic. By identifying pertinent opinionated keyphrases and employing advanced natural language processing (NLP) techniques, we sifted through over 3.9 million tweets to collect health informatics. The content of these tweets is analyzed to investigate the word trends and sentiments. The extracted sentiments were then organized into ten distinct categories using the K-means clustering method that pointed out top-ten healthcare supply needs. The findings underscore the value of social media as a tool for gathering timely and actionable insights on healthcare supply chain logistics, offering critical data for effective response strategies during health crises.

关键词： COVID-19 Social networking (online) Pandemics Hospitals Supply chains Training data Medical services natural language processing Real-time systems Testing

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Argument Mining using BERT and Self-Attention based Embeddings 4

Argument Mining using BERT and Self-Attention based Embeddin...

引用

4th international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Srivastava, Pranjal Bhatnagar, Pranav Goel, Anurag Delhi Technological University Department of Computer Science and Engineering New Delhi110042 India

ISBN: (纸本)9781665474368

Argument mining automatically identifies and extracts the structure of inference and reasoning conveyed in natural language arguments. To the best of our knowledge, most of the state-of-the-art works in this field have focused on using tree-like structures and linguistic modeling. But, these approaches are not able to model more complex structures which are often found in online forums and real world argumentation structures. In this paper, a novel methodology for argument mining is proposed which employs attention-based embeddings for link prediction to model the causational hierarchies in typical argument structures prevalent in online discourse. © 2022 ieee.

关键词： Embeddings

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A Big Wave of Deep Learning in Medical Imaging - Analysis of Theory and Applications 6

A Big Wave of Deep Learning in Medical Imaging - Analysis of...

引用

6th international conference on Intelligent Computing and Control Systems, ICICCS 2022

作者： Senthil, G.A. Prabha, R. Razmah, M. Sridevi, S. Roopa, D. Asha, R.M. Dept of Electronics and Communication Engineering Chennai India Department of Computer Science and Engineering Chennai India Sri Sairam Institute of Technology Dept of Computer Science and Engineering Chennai India Sri Sairam Institute of Technology Dept of Civil Engineering Chennai India

ISBN: (纸本)9781665410359

Deep learning models have become the province of cutting-edge machine learning models that are widely used in medical imaging in multiple kinds of ranging from image recognition to natural language processing. This article presents a brief overview on the recent developments and some relevant issues in medical image processing and image analysis as they related to machine learning. The proposed article will not cover the entire application landscape because it is becoming a very large and rapidly growing field, but will instead focus primarily on deep learning theory and techniques in medical imaging. Our goal is threefold: (i) To provide such a brief overview to deep medical imaging learning;(ii) To represent different approaches as well as comparisons of how deep learning has been accomplished, from image retrieval to segmentation to disease prediction;(iii) Provide the current medical imaging-related deep learning applications. © 2022 ieee.

关键词： Big data

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Research on Business Process Optimization Model of Customer Service Center of Power Enterprises Based on Deep Learning

Research on Business Process Optimization Model of Customer ...

引用

Electronics, Energy Systems and Power engineering (EESPE), ieee international conference on

作者： Hui Zhang Weiming Du Xiaoyu Xia Xin Wang Zhenyu Han State Grid Call Center Tianjin China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

This paper proposes a business process optimization model of customer service center of power enterprises based on deep learning, focusing on the development and application of automatic order extraction algorithm. The model aims to realize the automatic processing of omni-channel customer service data to meet the needs of balanced proportion of work orders in various provinces of business types and balanced work orders of the entire staff. Through the construction of DNN model, combined with CNN and LSTM technology, this paper realizes the automatic processing of customer service call content and the recognition of customer emotions. Experimental results show that the application of this model on actual data sets can significantly improve the quality inspection efficiency and accuracy of customer service center of power enterprises, promote the intelligent transformation of customer service business, and provide power enterprises with efficient and accurate business monitoring and decision support tools.

关键词： Deep learning Power engineering Accuracy Automation Customer services Optimization models natural language processing Data models Optimization Business

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Improving Chinese Named Entity Recognition by Glyph and Part-of-Speech 6

Improving Chinese Named Entity Recognition by Glyph and Part...

引用

6th international conference on Artificial Intelligence and Big Data, ICAIBD 2023

作者： Tan, Qiurong Li, Yuanyuan Xiong, Xi Wang, Jianghe He, Ling Huang, Feihu Chengdu University of Information Technology School of Cybersecurity Chengdu China West China Hospital of Sichuan University Mental Health Center Chengdu China Sichuan University College of Biomedical Engineering Chengdu China Sichuan University College of Computer Science Chengdu China

ISBN: (纸本)9781665491259

Chinese characters are a unique type of morpheme-syllabic characters that are integral to the ideographic writing system used in the Chinese language. The most widely used word formation method in Chinese is the formation of compound words from roots according to syntactic relationships, which is basically the same as the syntax of combining words into phrases. However, the appropriate use of glyph information and lexical information in natural language processing tasks is still limited. To solve above problem, this paper proposes a novel approach, named GP-NER, to enhance Chinese character representation by glyph features and inject word information with part-of-speech information. Specifically, GP-NER uses a gate mechanism to enhance the representation of characters by combining glyph features, and then uses part-of-speech information to enhance the contextual representation of text sequences through attention mechanisms. Experimental results demonstrate the effectiveness of the proposed model. © 2023 ieee.

关键词： Syntactics

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Sentiment and knowledge Based Algorithmic Trading with Deep Reinforcement Learning 33rd

Sentiment and Knowledge Based Algorithmic Trading with Deep ...

引用

33rd international conference on Database and Expert Systems Applications (DEXA)

作者： Nan, Abhishek Perumal, Anandh Zaiane, Osmar R. Univ Alberta Edmonton AB Canada Alberta Machine Intelligence Inst Edmonton AB Canada

ISBN: (纸本)9783031124235;9783031124228

Algorithmic trading, due to its inherent nature, is a difficult problem to tackle;there are too many variables involved in the real-world which makes it almost impossible to have reliable algorithms for automated stock trading. The lack of reliable labelled data that considers physical and physiological factors that dictate the ups and downs of the market, has hindered the supervised learning attempts for dependable predictions. To learn a good policy for trading, we formulate an approach using reinforcement learning which uses traditional time series stock price data and combines it with news headline sentiments, while leveraging knowledge graphs for exploiting news about implicit relationships.

关键词： Reinforcement learning Trading Stock price prediction Sentiment analysis knowledge graph natural language processing

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***: knowledge Graphs Creation using Large language Models

***: Knowledge Graphs Creation using Large Language Models

引用

Distributed Computing and Electrical Circuits and Electronics (ICDCECE), ieee international conference on

作者： Vaishali Jadhav Neandra Robert Kalluvital Lindsey Pereira Blossom Fernandes Nimisha Naik Department of Information Technology St. Francis Institute of Technology Mumbai India

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

Large language Models (LLMs) like GPT-4, built upon the Transformer architecture [1], have revolutionized natural language processing but often lack direct access to structured factual knowledge [2]." knowledge graphs (KGs) offer structured data but struggle with scalability, evolution, and updates. Integrating LLM with KG enhances knowledge retrieval, reasoning, and adaptability. This research proposes a question-answering system using Retrieved Generation (RAG) and Neo4j to bridge LLM and KG. The system refines entity extraction, relationship mapping, and dynamic KG updates, improving contextual relevance and precision. Performance evaluations show that GPT-4 outperforms other models, achieving 90% accuracy and 88% F1 score, demonstrating strong language understanding and relationship extraction, although with 85% specificity. Advanced node filtering and query interpretation improve accuracy, particularly in complex domains such as legal documents. A user-friendly interface facilitates efficient node access and data export, ensuring seamless knowledge utilization. The proposed system advances AI-driven knowledge retrieval by leveraging GPT-4’s adaptability and KGs’ structured knowledge, making question-answering tasks more reliable, scalable, and context-aware. This approach significantly improves the usability and effectiveness of AI systems in handling complex queries across various domains.

关键词： Technological innovation Ethics Accuracy Text analysis Law Large language models Semantics Decision making knowledge graphs Usability

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