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

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natural language Dialogue Model Based on Multi-Head Graph Attention Neural Network 24

Natural Language Dialogue Model Based on Multi-Head Graph At...

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2nd international conference on Advances in Artificial Intelligence and Applications, AAIA 2024

作者： Tao, Jie Cui, Peizhang Wang, Xin Army Engineering University Shijiazhuang Campus shijiazhuang China

ISBN: (纸本)9798400712883

The practical application of artificial intelligence in the field of natural language processing is becoming more and more extensive, and the technology is changing day by day. With the rapid update and development of artificial intelligence technology, more and more researchers find that the use of graph neural networks to construct a natural language dialog model can obtain better results than other artificial intelligence techniques, due to the fact that the deep neural network model is too large and complex, which hinders the practical application of artificial intelligence technology in the field of natural language dialog, in this paper, we propose a natural language dialog model based on the multi-head graph attention neural network This paper proposes a natural language dialog model based on multi-head graph attention neural network, which verifies the practicability and robustness of the method after simulation experiments. © 2024 Copyright held by the owner/author(s).

关键词： Neural network models

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Assistance for Visually Impaired individuals using Artificial Intelligence and natural language processing

Assistance for Visually Impaired individuals using Artificia...

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2025 IEEE international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Sathvig, Sri K. Bharathi.ln, Priyanka Santhosh.m Ramya, J. St. Joseph's College of Engineering Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331529833

People with Visual Impairments are vulnerable to various challenges in their everyday lives ranging from Access to Information, Travelling to places, Recognizing their surroundings, Isolated Lifestyle and much more. The Proposed solution aims to address these socially inclusive problems through the use of Artificial Intelligence, natural language processing, Machine Learning and Computer Vision. The feature index involves using Object Detection models like Yolo v5 for Obstacle avoidance, Integration of Google Map API for Seamless Navigation and NLP based Optical Character Recognition (OCR) for Text Recognition. An average accuracy of 86 percentage was achieved across all models. This solution aims to provide Better Lifestyle, Increased accessibility and Reduced Dependability for the Community of Visually Impaired Individuals. © 2025 IEEE.

关键词： Artificial Intelligence Computer Vision Machine Learning natural language processing

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Enhancing Communication and Data Transmission Security in RAG Using Large language Models 4

Enhancing Communication and Data Transmission Security in RA...

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4th international conference on Sustainable Expert Systems, ICSES 2024

作者： Gummadi, Venkata Udayaraju, Pamula Sarabu, Venkata Rahul Ravulu, Chaitanya Seelam, Dhanunjay Reddy Venkataramana, S. Expedia Group Austin United States School of Engineering and Sciences Srm University-AP Department of Cse India Wallmart Global Tech TX United States Rocket Companies MI United States Wallmart Global Tech BentonvilleAR United States Srkr Engineering College Department of It Bhimavaram India

ISBN: (纸本)9798331540364

Retrieval-augmented generation (RAG) enhances large language models (LLMs) by integrating external knowledge sources, enabling more useful information and generating accurate responses. This paper explores RAG's architecture and applications, combining generator and retriever models to access and utilize vast external data repositories. While RAG holds significant promise for various natural language processing (NLP) processes like dialogue generation, summarization, and question answering, it also presents unique security challenges that must be addressed to ensure system integrity and reliability. RAG systems face several security threats, including data poisoning, model manipulation, privacy leakage, biased information retrieval, and harmful outputs generation. Generally, in the traditional RAG application, security threat is one of the major concerns. To tighten the security system and enhance the efficiency of the model on processing more complex data this paper outlines key strategies for securing RAG-based applications to mitigate these risks paper outlines key strategies for securing RAG-based applications to mitigate these risks. Ensuring data security through filtering, sanitization, and provenance tracking can prevent data poisoning and enhance the quality of external knowledge sources. Strengthening model security via adversarial training, input validation, and anomaly detection improves resilience against manipulative attacks. Implementing output monitoring and filtering techniques, such as factual verification, language moderation, and bias detection, ensures the accuracy and safety of generated responses. Additionally, robust infrastructure and access control measures, including secure data storage, secure APIs, and regulated model access, protect against unauthorized access and manipulation. Moreover, this study analyzes various use cases for LLMs enhanced by RAG, including personalized recommendations, customer support automation, content creation,

关键词： natural language processing systems

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LlmRe: A zero-shot entity relation extraction method based on the large language model 7

LlmRe: A zero-shot entity relation extraction method based o...

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7th international conference on Electronic Information Technology and Computer engineering (EITCE)

作者： Zhao, Wei Chen, Qinghui You, Junling Zhengzhou Univ Sch Cyber Sci & Engn Zhengzhou Peoples R China

ISBN: (纸本)9798400708305

Entity relation extraction aims to extract knowledge triples from unstructured or semi-structured text data and can be applied to various fields, including medicine, finance knowledge graph construction and intelligent question-answering. Traditional entity relation extraction requires a large amount of labeled data, consumes a lot of labor and time, and the trained model lacks generalization ability, which is difficult to migrate to other fields. Zero-shot entity relation extraction relieves the dependence on labeled data in traditional method. Based on unlabeled text data, zero-shot entity relation extraction has strong domain adaptability, which is a very challenging and practical task. Recent work on large language models shows that large models can effectively complete downstream tasks through natural language instructions and have good generalization ability. Inspired by this, we explore the use of large models for information extraction. Due to the randomness of large language model generation, we introduce in-context learning in entity relation extraction task to guide large language model to output data in a specified format to help obtain structured data. At the same time, we propose a three-stage extraction framework for decomposing entity relation extraction tasks, and each stage is conducted in the form of question and answer to reduce the complexity of extraction. We evaluated the knowledge triples extraction performance of the model on three self-built test datasets in different fields, and the experimental result showed that our proposed method achieved impressive performance in the zero-shot entity relation extraction task, surpassing the comparison model on multiple metrics, proving the effectiveness and domain adaptability of the proposed method.

关键词： natural language processing Entity relation extraction Large language model Information extraction In-context learning mutistage framework

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3D-EX: A Unified Dataset of Definitions and Dictionary Examples

3D-EX: A Unified Dataset of Definitions and Dictionary Examp...

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2023 international conference Recent Advances in natural language processing: Large language Models for natural language processing, RANLP 2023

作者： Almeman, Fatemah Sheikhi, Hadi Espinosa-Anke, Luis CardiffNLP School of Computer Science and Informatics Cardiff University United Kingdom College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Saudi Arabia School of Computer Engineering Iran University of Science and Technology Iran AMPLYFI United Kingdom

ISBN: (纸本)9789544520922

Definitions are a fundamental building block in lexicography, linguistics and computational semantics. In NLP, they have been used for retrofitting word embeddings or augmenting contextual representations in language models. However, lexical resources containing definitions exhibit a wide range of properties, which has implications in the behaviour of models trained and evaluated on them. In this paper, we introduce 3D-EX, a dataset that aims to fill this gap by combining well-known English resources into one centralized knowledge repository in the form of triples. 3D-EX is a unified evaluation framework with carefully pre-computed train/validation/test splits to prevent memorization. We report experimental results that suggest that this dataset could be effectively leveraged in downstream NLP tasks. Code and data are available at https://***/F-Almeman/3D-EX. © 2023 Incoma Ltd. All rights reserved.

关键词： natural language processing systems

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Efficient Textual Similarity using Semantic MinHashing

Efficient Textual Similarity using Semantic MinHashing

引用

international conference on Big Data and Smart Computing (BigComp)

作者： Nawaz, Waqas Baig, Maryam Khan, Kifayat Ullah Islamic Univ Madinah Dept Informat Syst Madinah Saudi Arabia NUCES FAST Univ Dept Comp Sci Islamabad Pakistan Birmingham City Univ Birmingham City Business Sch Coll Accountancy Finance & Econ Birmingham England

ISBN: (纸本)9798350370027;9798350370034

Quantifying the likeness between words, sentences, paragraphs, and documents plays a crucial role in various applications of natural language processing (NLP). As Bert, Elmo, and Roberta exemplified, contemporary methodologies leverage neural networks to generate embeddings, necessitating substantial data and training time for cutting-edge performance. Alternatively, semantic similarity metrics are based on knowledge bases like WordNet, using approaches such as the shortest path between words. MinHashing, a nimble technique, quickly approximates Jaccard similarity scores for document pairs. In this study, we propose employing MinHashing to gauge semantic scores by enhancing original documents with information from semantic networks, incorporating relationships such as synonyms, antonyms, hyponyms, and hypernyms. This augmentation improves lexical similarity based on semantic insights. The MinHash algorithm calculates compact signatures for extended vectors, mitigating dimensionality concerns. The similarity of these signatures reflects the semantic score between the documents. Our method achieves approximately 64% accuracy in the MRPC and SICK data sets.

关键词： MinHashing Semantic similarity WordNet natural language processing (NLP) Jaccard similarity Algorithm

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A Novel natural language processing Based Model for Plagiarism Detection 6

A Novel Natural Language Processing Based Model for Plagiari...

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6th IEEE international conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Patil, Rahul Kadam, Vaibhavi Nakate, Roshan Kadam, Sanjay Pattade, Suraj Mitkari, Mihir Bharati Vidyapeeth College of Engineering Navi Mumbai India

ISBN: (纸本)9798350306613

In order to detect plagiarism in academic and professional contexts, this work presents an intelligent natural language processing (NLP) method. The program outperforms conventional exact match algorithms by utilizing sophisticated NLP techniques, robust preprocessing and semantic analysis. It employs AHP, examines linguistic complexity, and identifies paraphrasing. Inspired by previous research, the strategy examines multi-word phrases and how frequently they are used to gain a more in-depth understanding. We contrast reputable and suspicious studies, assigning varying weights to different areas. Our comprehensive analysis demonstrates that the program performs admirably across a range of topic areas, improving plagiarism detection and maintaining the integrity of academic papers. © 2024 IEEE.

关键词： AHP natural language processing Plagiarism Detection Semantic Analysis

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JOINT MULTI-FACTS REASONING NETWORK FOR COMPLEX TEMPORAL QUESTION ANSWERING OVER knowledge GRAPH 49

JOINT MULTI-FACTS REASONING NETWORK FOR COMPLEX TEMPORAL QUE...

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49th IEEE international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Huang, Rikui Wei, Wei Quo, Xiaoye Xie, Wenfeng Mao, Xianling Chen, Dangyang Huazhong Univ Sci & Technol Inst Artificial Intelligence Wuhan Hubei Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan Hubei Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Hubei Peoples R China Ping An Property & Casualty Insurance Co China Lt Shenzhen Peoples R China Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Temporal knowledge Graph (TKG) is an extension of regular knowledge graph by attaching the time scope. Existing temporal knowledge graph question answering (TKGQA) models solely approach simple questions, owing to the prior assumption that each question only contains a single temporal fact with explicit/implicit temporal constraints. Hence, they perform poorly on questions which own multiple temporal facts. In this paper, we propose Joint Multi Facts Reasoning Network (JMFRN), to jointly reasoning multiple temporal facts for accurately answering complex temporal questions. Specifically, JMFRN first retrieves question-related temporal facts from TKG for each entity of the given complex question. For joint reasoning, we design two different attention (i.e., entity-aware and time-aware) modules, which are suitable for universal settings, to aggregate entities and timestamps information of retrieved facts. Moreover, to filter incorrect type answers, we introduce an additional answer type discrimination task. Extensive experiments demonstrate our proposed method significantly outperforms the state-of-art on the well-known complex temporal question benchmark TimeQuestions.

关键词： Temporal knowledge graph question answering knowledge graph neural language processing

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Abstract-level Deductive Reasoning for Pre-trained language Models 30

Abstract-level Deductive Reasoning for Pre-trained Language ...

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

作者： Wu, Xin Cai, Yi Leung, Ho-Fung School of Software Engineering South China University of Technology China Ministry of Education China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9782493814104

Pre-trained language Models have been shown to be able to emulate deductive reasoning in natural language. However, PLMs are easily affected by irrelevant information (e.g., entity) in instance-level proofs when learning deductive reasoning. To address this limitation, we propose an Abstract-level Deductive Reasoner (ADR). ADR is trained to predict the abstract reasoning proof of each sample, which guides PLMs to learn general reasoning patterns rather than instance-level knowledge. Experimental results demonstrate that ADR significantly reduces the impact of PLMs learning instance-level knowledge (over 70%). © 2024 ELRA language Resource Association: CC BY-NC 4.0.

关键词： Computational linguistics

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Towards an Approach for Extending BPMN with Quality Dimension 3rd

Towards an Approach for Extending BPMN with Quality Dimensio...

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3rd international conference on language processing and knowledge Management-LPKM

作者： Alyani, Zohra Turki, Mohamed Dhouib, Karima Gargouri, Faiez MIRACL Lab FSEG Sfax Sfax Tunisia Univ Hail Colledge Comp Sci & Engn Hail 81451 Saudi Arabia ISIM Sfax MIRACL Lab Sfax Tunisia

ISBN: (纸本)9783031850660;9783031850677

Building a quality management system (QMS) relies heavily on two key steps: mapping and modeling business processes. The process approach, a core principle of QMS, emphasizes identifying and managing processes and their interactions systematically. This ensures achieving desired outcomes aligned with the organization's policies and strategic goals. Following this approach, organizations need to establish, implement, maintain, and continuously improve their quality management system (SQM), encompassing all relevant processes and their connections, to meet regulatory requirements. This article aims to present our approach for extending the BPMN formalism by a quality dimension based on the MDA approach and on an ontological approach for the definition of a meta-model for the quality management which covers the ISO 9001 Norm.

关键词： Quality Management Business process modeling Ontology Core quality ISO 9001 Ontological engineering BPMN2.0

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