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检索条件"任意字段=International Conference on Natural Language Processing and Knowledge Engineering"

共 26903 条记录，以下是251-260 订阅

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natural language processing for Infrastructure Resilience to natural Disasters: A Scientometric Review 7th

Natural Language Processing for Infrastructure Resilience to...

引用

7th international conference on Geotechnics, Civil engineering and Structures, CIGOS 2024

作者： Moriyani, Muhammad Ali Asaye, Lemlem Le, Chau Le, Tuyen Le, Trung North Dakota State University FargoND58102 United States Clemson University ClemsonSC29634 United States University of South Florida TampaFL33620 United States

ISBN: (纸本)9789819719716

New solutions are needed to enhance infrastructure resilience in response to global warming and increasing natural disasters. Recent studies demonstrate the potential of natural language processing (NLP) for mining unstructured human language during a natural disaster to assist with emergency decision-making, such as identifying infrastructure damages. NLP-based social sensing offers a way to detect disruptions and gain insights into situational awareness about critical infrastructure. Nonetheless, few studies have attempted to map the global research on applying NLP for infrastructure resilience. This paper aims to fill this gap by presenting a scientometric review of current knowledge using networks derived from the bibliographic records of the Scopus and Web of Science core collection. Specifically, this review highlights the top trends, temporal variations, significant developments, challenges, and future opportunities in NLP-based infrastructure resilience research. This study intends to improve the understanding of using NLP for infrastructure resilience against natural disasters. © The Author(s).

关键词： Disasters

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Open-Source Large language Models: A Comprehensive Survey 2

Open-Source Large Language Models: A Comprehensive Survey

引用

2nd international conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Sahana Lokesh, R. Siddaganga Institute of Technology Department of Computer Science and Engineering Karnataka Tumakuru India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505745

Large language Models (LLMs) have emerged as strategic in the promotion of natural language processing (NLP) to the extent of having machines translate languages as well as making logical deductions. The role of the open-source team in advancing this progress is that LLMs are now available for development and research. This paper offers an elaborate literature review on some of the most widely used open-source LLMs and their architecture, training approach, and evaluation results. Certainly, the mathematical aspects behind these models are explored, the comparison of the models is given and the problems and perspectives of development of the models are described. © 2025 IEEE.

关键词： natural language processing systems

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Efficiency & Innovation in Recruitment usingAI & Android

Efficiency & Innovation in Recruitment usingAI & Android

引用

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

作者： Chalana, Kanika Gautam, Ashwin Ravi Raj, Anand Daivik, J.S. Amrutha, H.R. Dayananda Sagar College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350359688

The goal of screening resumes is to determine the top applicants for a position and to inform users of their resume score and areas for improvement. The literature on existing approaches has been analyzed, and it has been discovered that the traditional systems like manual screening may result in false assumptions and the wasting of human potential, but they lackrobustness Regarding processing, accuracy and efficiency. To acquire accurate results, software must utilized to process natural language and machine learning techniques to match and rate the candidates in real-time by ranking their resumes. The input would be the resumes and output would be a ranked candidate's resumes on the admin side and recommendations for the user side. Instantaneous real-time output results are acquired by employing Object character recognition. In The system that is being suggested the authors used OCR model and android principles along with python scripts. This system has the many benefits including security, interpretability, high accuracy, lightweight model, and quick processing. © 2024 IEEE.

关键词： Adversarial machine learning

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Large language Model-Enabled Multi-Agent Manufacturing Systems 20

Large Language Model-Enabled Multi-Agent Manufacturing Syste...

引用

IEEE 20th international conference on Automation Science and engineering (CASE)

作者： Lim, Jonghan Vogel-Heuser, Birgit Kovalenko, Ilya Penn State Univ Dept Ind & Mfg State Coll PA 16802 USA Penn State Univ Dept Mech Engn State Coll PA 16802 USA Tech Univ Munich Inst Automat & Informat Syst Munich Germany

ISBN: (纸本)9798350358513;9798350358520

Traditional manufacturing faces challenges adapting to dynamic environments and quickly responding to manufacturing changes. The use of multi-agent systems has improved adaptability and coordination but requires further advancements in rapid human instruction comprehension, operational adaptability, and coordination through natural language integration. Large language models like GPT-3.5 and GPT-4 enhance multi-agent manufacturing systems by enabling agents to communicate in natural language and interpret human instructions for decision-making. This research introduces a novel framework where large language models enhance the capabilities of agents in manufacturing, making them more adaptable, and capable of processing context-specific instructions. A case study demonstrates the practical application of this framework, showing how agents can effectively communicate, understand tasks, and execute manufacturing processes, including precise G-code allocation among agents. The findings highlight the importance of continuous large language model integration into multi-agent manufacturing systems and the development of sophisticated agent communication protocols for a more flexible manufacturing system.

关键词： Flexible manufacturing systems

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Integrating Open-domain knowledge via Large language Model for Multimodal Fake News Detection 27

Integrating Open-domain Knowledge via Large Language Model f...

引用

27th international conference on Computer Supported Cooperative Work in Design (CSCWD)

作者： Xie, Anbin Zhu, Fuqing Han, Jizhong Hu, Songlin Chinese Acad Sci Inst Informat Engn Beijing 100085 Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing 100049 Peoples R China

ISBN: (纸本)9798350349184;9798350349191

Multimodal fake news propagation on social media has emerged as a primary concern for both the public and the government. Distinguishing fake news from genuine content is challenging due to their close resemblance. Moreover, fake news often presents information that contradicts real-world facts, underscoring the necessity of incorporating comprehensive open-domain knowledge. However, current knowledge-based detection methods struggle to improve two key areas in integrating open-domain knowledge for fake news detection: i) query acquisition for retrieval and ii) utilization of retrieved knowledge. The challenges stem from limitations in understanding the semantics and reasoning about the relationship between open-domain knowledge and the content of the news. With the emergence of the Large language Model (LLM), natural language processing (NLP) has witnessed a revolution where impressive semantic understanding and reasoning abilities are significantly improved. This paper proposes a novel Open-domain knowledge Integrated (OKI) framework for multimodal fake news detection, featuring two LLM-based agents that collaboratively leverage open-domain knowledge. One agent generates appropriate queries to retrieve knowledge, while the other filters out irrelevant retrieved knowledge. Experimental results demonstrate a significant performance improvement of OKI over established baselines on the Weibo and Twitter datasets.

关键词： open-doamin knowledge multimodal fake news detection Large language Model (LLM)

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Pre-training language Model for Mongolian with Agglutinative Linguistic knowledge Injection

Pre-training Language Model for Mongolian with Agglutinative...

引用

international Joint conference on Neural Networks (IJCNN)

作者： Na, Muhan Liu, Rui Bao, Feilong Gao, Guanglai Inner Mongolia Univ Coll Comp Sci Hohhot Peoples R China Natl & Local Joint Engn Res Ctr Intelligent Infor Hohhot Peoples R China Inner Mongolia Key Lab Mongolian Informat Proc Te Hohhot Peoples R China

ISBN: (纸本)9798350359329;9798350359312

BERT based Pre-training language Model (PLM) has become a crucial step in achieving the best results in various natural language processing (NLP) tasks. However, the current progress, which mainly focuses on major languages such as English and Chinese, has not thoroughly investigated the low-resource languages, particularly agglutinative languages like Mongolian, due to the scarcity of large-scale data resources and the difficulty of understanding agglutinative knowledge. In this paper, we propose a novel PLM for the Mongolian language, that incorporates a novel three-stage agglutinative knowledge injection strategy. Specifically, early-stage injection aims to convert the Mongolia word sequence to the fine-grained sub-word token that comprises a stem and some suffixes;Middle-stage injection designed a morphological knowledge-based masking strategy to enhance the model's ability to learn agglutinative knowledge;Late-stage injection not only involves the model restoring the masked tokens but also predicting the order of suffixes. To address the issue of data scarcity, we create a large-scale Mongolian PLM dataset and three datasets for three downstream tasks, that are News Classification, Name Entity Recognition (NER), and Part-of-Speech (POS) prediction, etc. The experimental results on three downstream tasks demonstrate that our method surpasses the traditional BERT approach and successfully learns agglutinative language knowledge in Mongolian.

关键词： Pre-training language model,Mongolian Agglutinative Linguistic knowledge

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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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2025 international conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Subhashini, P. Veeresh, G. Lakhanpal, Sorabh Sharma, Nidhi Saeed, Hazim Y. Soujanya, Rajireddy Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India New Horizon College of Engineering Bangalore India Lovely Professional University Phagwara India Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq Griet Department of Cse Bachupally Telangana Hyderabad India

ISBN: (纸本)9798331527495

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. © 2025 IEEE.

关键词： natural language processing systems

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How Large language Models Encode Context knowledge? A Layer-Wise Probing Study 30

How Large Language Models Encode Context Knowledge? A Layer-...

引用

Joint 30th international conference on Computational Linguistics and 14th international conference on language Resources and Evaluation, LREC-COLING 2024

作者： Ju, Tianjie Sun, Weiwei Du, Wei Yuan, Xinwei Ren, Zhaochun Liu, Gongshen School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China Shandong University China Southeast University China Leiden University Netherlands

ISBN: (纸本)9782493814104

Previous work has showcased the intriguing capability of large language models (LLMs) in retrieving facts and processing context knowledge. However, only limited research exists on the layer-wise capability of LLMs to encode knowledge, which challenges our understanding of their internal mechanisms. In this paper, we devote the first attempt to investigate the layer-wise capability of LLMs through probing tasks. We leverage the powerful generative capability of ChatGPT to construct probing datasets, providing diverse and coherent evidence corresponding to various facts. We employ V-usable information as the validation metric to better reflect the capability in encoding context knowledge across different layers. Our experiments on conflicting and newly acquired knowledge show that LLMs: (1) prefer to encode more context knowledge in the upper layers;(2) primarily encode context knowledge within knowledge-related entity tokens at lower layers while progressively expanding more knowledge within other tokens at upper layers;and (3) gradually forget the earlier context knowledge retained within the intermediate layers when provided with irrelevant evidence. Code is publicly available at https://***/Jometeorie/probing_llama. © 2024 ELRA language Resource Association: CC BY-NC 4.0.

关键词： Modeling languages

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language Model Embeddings to Improve Performance in Downstream Tasks 16

Language Model Embeddings to Improve Performance in Downstre...

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16th IEEE international conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Pawar, Abhishek Patil, Parag Hiwanj, Riya Kshatriya, Ajinkya Chikmurge, Diptee Barve, Sunita Mit Academy of Engineering Alandi Pune India Ljmu University Liverpool United Kingdom

ISBN: (纸本)9798331505264

natural language processing (NLP) has made significant breakthroughs, mainly in the creation of transformer-based language models, and has demonstrated great performance in a variety of language comprehension tasks. Despite these achievements, maximizing the possibilities for downstream applications remains an ongoing problem. While models based on transformers excel at learning contextualized representations during pretraining, they can benefit from focused modifications in downstream tasks. Our study highlights the advantages of domain-specific model embeddings over fine-tuning language models based on generalized transformers. The study methodology entails a detailed examination of textual datasets, including Marathi news corpus, medical text, and legal references. Sophisticated cleaning procedures are applied to enhance model performance. The proposed end-to-end pipeline comprises validation, classification, RoBERTa fine-tuning, and data selection for downstream processes. Through a more nuanced knowledge of the evolving field of natural language processing (NLP), this research advances our understanding of the effectiveness and adaptability of refined transformer-based language model embeddings in real-world applications. © 2024 IEEE.

关键词： Embeddings

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Research on fault Q&A system for train-controlled on-board equipment based on knowledge graph 9

Research on fault Q&A system for train-controlled on-board e...

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9th international conference on Electromechanical Control Technology and Transportation, ICECTT 2024

作者： Zhi, Yingyuan Chen, Yonggang Wang, Haiyong School of Automation and Electrical Engineering Lanzhou Jiaotong University Lanzhou730070 China School of Electonic and Information Engineering Lanzhou Jiaotong University Lanzhou730070 China

ISBN: (纸本)9781510682399

At present, the presentation of the knowledge graph of train-controlled on-board equipment is only one model in most cases, the entity extraction process is more complicated, and there is a lack of Q&A system and other related practical applications. In view of the above problems, the paper proposes to apply the deep learning model to the entity recognition in order to improve the accuracy rate, F1, and increase the faults of the other four models to make the presentation of the graph more complete, and ultimately combine the knowledge graph, graph database, natural language processing, knowledge quiz and other technologies are combined with this field. This paper adopts entity extraction,relationship extraction, entity alignment and knowledge storage to create a knowledge map of train-controlled on-board equipment faults, and accordingly realises the construction of a Q&A system to provide auxiliary decision-making for railway maintenance personnel. © 2024 SPIE.

关键词： knowledge graph

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