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Semantic Information Extraction for Text Data with Probability Graph

Semantic Information Extraction for Text Data with Probabili...

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2023 IEEE/CIC international conference on Communications in China, ICCC Workshops 2023

作者： Zhao, Zhouxiang Yang, Zhaohui Hu, Ye Lin, Licheng Zhang, Zhaoyang Zhejiang University College of Information Science and Electronic Engineering Hangzhou China Zhejiang Lab Hangzhou China Hangzhou China University of Miami Department of Industrial and System Engineering Coral Gables United States

ISBN: (纸本)9798350345407

In this paper, the problem of semantic information extraction for resource constrained text data transmission is studied. In the considered model, a sequence of text data need to be transmitted within a communication resource-constrained network, which only allows limited data transmission. Thus, at the transmitter, the original text data is extracted with natural language processing techniques. Then, the extracted semantic information is captured in a knowledge graph. An additional probability dimension is introduced in this graph to capture the importance of each information. This semantic information extraction problem is posed as an optimization framework whose goal is to extract most important semantic information for transmission. To find an optimal solution for this problem, a Floyd's algorithm based solution coupled with an efficient sorting mechanism is proposed. Numerical results testify the effectiveness of the proposed algorithm with regards to two novel performance metrics including semantic uncertainty and semantic similarity. © 2023 IEEE.

关键词： knowledge graph

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A Real-time Medical Report Analysis and AI-Powered Diagnosis: A Cloud-Based Solution forImproved Patient Care 2

A Real-time Medical Report Analysis and AI-Powered Diagnosis...

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2nd IEEE international conference on Advances in Information Technology, ICAIT 2024

作者： Mahalakshmi, M. Bharadwaj, Shardul Bhuyan, Aklanta Niraz Department of Networking and Communication College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350383867

The ever-growing deluge of medical records overwhelms healthcare professionals, jeopardizing timely diagnoses and optimal treatment. This paper proposes an innovative cloud-based solution that automates medical report analysis and leverages Artificial Intelligence (AI) to deliver relevant insights, revolutionizing patient care. The system integrates a robust natural language processing (NLP) engine within an Extract, Transform, Load (ETL) pipeline. This pipeline seamlessly extracts vital information from unstructured patient reports and test results, meticulously standardizing it for streamlined interoperability. This rich, structured data then fuels Large language Models (LLM). Using Retrieval Augmented Generation (RAG) we can generate accurate insights into patient reports. These models generate concise summaries, highlighting key clinical findings, and confidently suggest potential diagnoses, aiding physiciansin rapid decision-making. The system incorporates interactive data visualizations powered by advanced tools like Quicksightto facilitate deeper exploration and knowledge discovery. Initial testing validates the system's efficacy, demonstrating its ability to accurately summarize critical findings and propose diagnoses with relatively high confidence. This solution holds immense potential to revolutionize healthcare efficiency, optimize resource allocation, and ultimately deliver superior patient outcomes by alleviating the burden of data analysis and providing intelligent medical AI support. © 2024 IEEE.

关键词： Ability testing

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Edge Intelligence-Driven Meteorological knowledge Graph for Real-Time Decision-Making 29

Edge Intelligence-Driven Meteorological Knowledge Graph for ...

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29th IEEE international conference on Parallel and Distributed Systems, ICPADS 2023

作者： Yu, Zheng Jiang, Jielin He, Bingkun Bilal, Muhammad Liu, Dongqing Nanjing University of Information Science and Technology School of Software Nanjing China Wuhan University School of Cyber Science and Engineering Wuhan China Lancaster University School of Computing and Communications Bailrigg United Kingdom Nanjing Meteorological Bureau Nanjing Meteorological Observatory Nanjing China

ISBN: (纸本)9798350330717

Meteorological decision-making is a crucial element in the meteorological disaster warning and prevention field. With the increasing frequency of meteorological disasters and the rapid development of edge intelligence, there is an urgent need to establish a meteorological early-warning platform that reduces human resource investment, decreases operating costs, and provides targeted information and response suggestions. Therefore, we propose the development of real-time decision-making based on edge intelligence-driven meteorological knowledge graph (EMKG), and aim to achieve meteorological emergency decision-making by combining the knowledge graph with edge intelligence. First, we collect data through edge devices and perform preprocessing and preliminary analysis on these devices to reduce the time and bandwidth requirements for data transmission to the cloud. Based on the above data, meteorological entity recognition and relation extraction were completed using techniques such as BERT, BiLSTM, CRF, and data augmentation. Then we trained a text generation model and deployed it on edge devices to achieve real-time meteorological decision-making. The experimental results show that EMKG effectively integrates edge intelligence and knowledge graph, and further improves the real-time and accuracy of meteorological decision-making. © 2023 IEEE.

关键词： knowledge graph meteorological emergency decision-making natural language processing

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An Extensive Study on Pretrained Models for natural language processing Based on Transformers

An Extensive Study on Pretrained Models for Natural Language...

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2022 international conference on Electronics and Renewable Systems, ICEARS 2022

作者： Ramprasath, M. Dhanasekaran, K. Karthick, T. Velumani, R. Sudhakaran, P. Srm Institute of Science and Technology Department of Data Science and Business Systems School of Computing TamilNadu Chennai India Department of Computer Science and Engineering Andhra Pradesh Madhrawada India Srm Trp Enginerring College Department of Computer Science and Engineering Tamil Nadu Trichy India

ISBN: (纸本)9781665484251

In recent years, Pretraining language Models based on Transformers -natural language processing (PLMT-NLP) have been highly successful in nearly every NLP task. In the beginning, Generative Pre-trained model-based Transformer, BERT- Bidirectional Encoder model Representations using Transformers was used to develop these models. Models constructed on transformers, Self-supervise knowledge acquiring, and transfer learning establish the foundation of these designs. Transformed-based pre-trained models acquire common linguistic illustrations from vast amounts of textual information through self-supervised model and apply this information to downstream tasks. To eliminate the need to retrain downstream models, these models provide a solid foundation of knowledge. In this paper, the enhanced learning on PLMT-NLP has been discussed. Initially, a quick introduction to self-supervised learning is presented, then diverse core concepts used in PLMT-NLP are explained. Furthermore, a list of relevant libraries for working with PLMT-NLP has been provided. Lastly, the paper discusses about the upcoming research directions that will further improve these models. Because of its thoroughness and relevance to current PLMT-NLP developments, this survey study will positively serve as a valuable resource for those seeking to understand both basic ideas and new developments better. © 2022 IEEE.

关键词： natural language processing systems

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Towards a Human-like Chatbot using Deep Adversarial Learning 14

Towards a Human-like Chatbot using Deep Adversarial Learning

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14th international conference on knowledge and Systems engineering, KSE 2022

作者： Tran, Quoc-Dai Luong Le, Anh-Cuong Huynh, Van-Nam Ton Duc Thang University Natural Language Processing and Knowledge Discovery Laboratory Faculty of Information Technology Ho Chi Minh City Viet Nam School of Knowledge Science Japan Advanced Institute of Science and Technology Japan

ISBN: (数字)9781665452816

ISBN: (纸本)9781665452809

Conversational agents are getting more popular and applied in a wide range of practical application areas. The main task of these agents is not only to generate context-appropriate responses to a given query but also to make the conversation human-like. Thanks to the ability of deep learning based models in natural language modeling, recent studies have made progress in designing conversational agents that can provide more semantically accurate responses. However, the naturalness in such conversation setting has not been given adequate attention in these studies. This paper aims to incorporate both important criteria of accuracy and naturalness of conversation in developing a new model for conversational agents. To this end, inspired by the idea of Turing test and the idea of adversarial learning strategy, we propose to design a model based on generative deep neural networks that interestingly allow to generate accurate responses optimized by the mechanics of imitating human-generated conversations. Experimental results demonstrate that the proposed models produce more natural and accurate responses, yielding significant gains in BLEU scores. © 2022 IEEE.

关键词： Generative adversarial networks

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Empowering Job Seekers by a natural language Solution for Personalized Job Descriptions and Enhanced Employment Prospects through Clustering Techniques

Empowering Job Seekers by a Natural Language Solution for Pe...

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2024 international conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Kaur, Upinder Bharany, Salil Vats, Satvik Kumar, Anand Kaushik, Keshav School of Computer Science and Engineering Lovely Professional University Punjab Phagwara144411 India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura India Department of Cse Graphic Era Hill University Dehradun India Graphic Era Deemed to be University Dehradun India Amity School of Engineering and Technology Amity University Punjab Mohali India

ISBN: (纸本)9798350379716

This Paper is based on our project which aims at implementing a natural language based solution which would deliver job descriptions based on a few criteria like preparing the transformed dataset, performing NLP processing to rank the best features in the Job Description, training transformer model and implementing an API endpoint which can be consumed over the web. The input data has been refined and data dictionaries have been created to generate Job description based on job title and skills. To anticipate the token that should come after the input, we have trained the GPT2 decoder transformer model to use the most recent token embedding of the input sequence. The study concludes that it is critical for job searchers to customize job descriptions according to an individual's talents, prior experiences, and educational background. While most of the research to date has focused on the client side of the hiring process, the suggested model seeks to empower job searchers by giving them customized job descriptions, which increases the likelihood that they will find acceptable employment. To provide insights into a variety of disciplines and help job searchers better grasp the dynamics of the recruiting process, the initiative made use of an extensive dataset. K-means clustering and DBScan clustering were two of the models and hyperparameters investigated within the project's time restrictions. K-means clustering improved context knowledge, albeit at the expense of adding noise to the data. Subsequent developments may entail investigating topic modeling and clustering strategies in greater detail to strengthen the model. Prospective directions for further research encompass utilizing Part of Speech (POS) tagging to extract distinct portions of job descriptions, implementing machine learning techniques to enhance synonym lists, and investigating substitute text creation models if preliminary efforts provide inconclusive results. With these developments, the suggested mod

关键词： Investments

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Revolutionizing Management Information Systems with natural language processing for Digital Transformation 27

Revolutionizing Management Information Systems with Natural ...

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27th international conference on knowledge Based and Intelligent Information and engineering Sytems, KES 2023

作者： Arslan, Muhammad Riaz, Zainab Cruz, Christophe Université de Bourgogne Dijon France Marketing Operations and Information Systems Department Abu Dhabi University Zayed City Abu Dhabi United Arab Emirates

natural language processing (NLP) is a rapidly growing field with immense potential to transform Management Information Systems (MIS) and drive digital transformation in businesses. To explore the possible applications of NLP for MIS, we first investigated current state-of-the-art research by examining the top-tier journals in the field. Our analysis revealed that while there has been some research exploring the benefits of NLP for MIS, the full potential of this powerful technology has not yet been fully realized in the literature. Therefore, this study aims to fill this gap by discussing the potential applications of NLP for MIS. We cover several scenarios, including boosting marketing campaigns, enhancing supplier relationship management, detecting misinformation, using competitive analysis to gain insights, identifying related contextual terms, and enhancing BI systems. The main goal of presenting various scenarios is to demonstrate the vast potential of NLP in transforming MIS and supporting digital transformation. Through this exploration of various scenarios, we hope to inspire researchers and practitioners to further explore the potential of NLP for MIS applications and unlock new possibilities for enhancing business operations, decision-making, and strategy development in the context of digital transformation. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Competitor Profiling Digital Transformation Management Information Systems natural language processing

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Dynamic knowledge Graph Construction for Real-Time natural language Understanding in Autonomous Systems

Dynamic Knowledge Graph Construction for Real-Time Natural L...

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Intelligent Control, Computing and Communications (IC3), international conference on

作者： P. Subhashini Veeresh G Sorabh Lakhanpal Nidhi Sharma Hazim Y. Saeed Rajireddy Soujanya Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India New Horizon College of Engineering Bangalore India Lovely Professional University Phagwara India Department of Electrical Electronics & Communication Engineering Galgotias university Gerater Noida Uttar Pradesh Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq Department of CSE GRIET Bachupally Hyderabad Telangana India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

This work presents Real- Time Semantic Mapping (RTSM), a new idea for improving the language comprehension in autonomous systems using the knowledge graph. RTSM solely relies on ConceptNet as its dataset and incorporate Spacy to parse and synchronize commends and signals from natural language appropriately. However, through the continuous updating of the knowledge graph, RTSM allows an autonomous system to provide accurate responding to real-time contextual changes with relatively low computational cost. Constant learning and semantic enhancement in this model make RTSM light and easily scalable, putting it at a higher ground as a natural language processing technique for use in autonomous systems. Due to a simple framework that makes it easy to incorporate all the knowledge, the RTSM is ideal in environments that are challenged for time and resources, but need to provide real time interaction capabilities.

关键词： Accuracy Autonomous systems Computational modeling Semantics Memory management knowledge graphs Real-time systems natural language processing Synchronization Intelligent control

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Utilizing Machine Learning for Advanced natural language processing and Sentiment Analysis in Social Media Platforms 1

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3rd international conference on Mechanical and Energy Technologies, ICMET 2023

作者： Manoharan, Geetha Jyothi, B. Swarna Bhat, Romica Acharjee, Purnendu Bikash Sharma, Ankush Rawal, Priyanka School of Business SR University Telangana Warangal India Rajeev Gandhi Memorial College of Engineering and Technology Nandyala India Amity School of Communication Amity University Kolkata Kolkata India Computer Science CHRIST University Pune Lavasa Campus Lavasa India Jagran Lakecity Business School Faculty of Management and Commerce Jagran Lakecity University Madhya Pradesh Bhopal India Faculty of Management and Commerce Jagran Lakecity University Madhya Pradesh Bhopal India

ISBN: (数字)9789819727162

ISBN: (纸本)9789819727155

Social media is increasingly regarded as one of the most abundant online resources for information gathering and knowledge exchange. Among the most widely used social media sites is Twitter available today. When attempting to comprehend the information in any unknown word-based data (such as social media), natural language processing (NLP) techniques are crucial since they help remove noise from data, identify stem words, etc. It also helps with comprehension of the sentiment or semantic contents. Using social media, we apply machine learning techniques (clustering and classification) to determine the viewpoint's polarity in the information. Several classifiers and clusters, including SVM, RF, Naive Byes, and KNN, are used to detect content on social media. Sentiment analysis is the process of automatically classifying user-generated content as neutral, negative, or positive. It is possible to utilize the text, sentence, feature, or aspect as criteria to group feelings into distinct categories. This study demonstrates the application of machine learning techniques to the analysis of emotions expressed on the Twitter network. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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FedMCP: Parameter-Efficient Federated Learning with Model-Contrastive Personalization 30

FedMCP: Parameter-Efficient Federated Learning with Model-Co...

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30th IEEE international conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhao, Qianyi Qu, Chen Chen, Cen Fan, Mingyuan Wang, Yanhao East China Normal University School of Data Science and Engineering Shanghai China University of Massachusetts Amherst College of Information & Computer Sciences AmherstMA United States

ISBN: (纸本)9798331515966

With increasing concerns and regulations on data privacy, fine-tuning pretrained language models (PLMs) in federated learning (FL) has become a common paradigm for NLP tasks. Despite being extensively studied, the existing methods for this problem still face two primary challenges. First, the huge number of parameters in large-scale PLMs leads to excessive communication and computational overhead. Second, the heterogeneity of data and tasks across clients poses a significant obstacle to achieving the desired fine-tuning performance. To address the above problems, we propose FedMCP, a novel parameter-efficient fine-tuning method with model-contrastive personalization for FL. Specifically, FedMCP adds two lightweight adapter modules, i.e., the global adapter and the private adapter, to the frozen PLMs within clients. In a communication round, each client sends only the global adapter to the server for federated aggregation. Furthermore, FedMCP introduces a model-contrastive regularization term between the two adapters. This, on the one hand, encourages the global adapter to assimilate universal knowledge and, on the other hand, the private adapter to capture client-specific knowledge. By leveraging both adapters, FedMCP can effectively provide fine-tuned personalized models tailored to individual clients. Extensive experiments on highly heterogeneous cross-task, cross-silo datasets show that FedMCP achieves substantial performance improvements over state-of-the-art FL fine-tuning approaches for PLMs. © 2024 IEEE.

关键词： Federated learning

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