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检索条件"任意字段=2003 International Conference on Natural Language Processing and Knowledge Engineering"

共 4384 条记录，以下是1-10 订阅

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Querying knowledge Graphs in Greek language 17

Querying Knowledge Graphs in Greek Language

引用

17th international conference on PErvasive Technologies Related to Assistive Environments (PETRA)

作者： Tsampos, Ioannis Marakakis, Emmanouil Hellen Mediterranean Univ Dept Elect & Comp Engn Khania Greece

ISBN: (纸本)9798400717604

We present a method for querying knowledge graphs through natural language, emphasizing its application to the Greek language. It integrates NLP techniques with the capabilities of graph databases to enable seamless interaction with knowledge graphs through natural language queries. Upon receiving a user's question in Greek language, we use linguistic analysis tools to convert it into a graph structure by employing predefined rules and adhering to a graph database schema. This methodology enables the handling of different question types and the efficient extraction of relations, ensuring accurate mapping of linguistic structures to database queries. The representation of a question as a knowledge graph enables its direct translation to a Cypher query, facilitating the extraction of related answers. The approach can handle complex questions and is language independent.

关键词： knowledge Graph Construction natural language processing knowledge Representation Question Answering Greek language Graph Databases Query languages

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Distilled BERT Model In natural language processing 14

Distilled BERT Model In Natural Language Processing

引用

14th international conference on Computer and knowledge engineering, ICCKE 2024

作者： Vakili, Yazdan Zandiye Fallah, Avisa Sajedi, Hedieh School of Mathematics Statistics and Computer Science University of Tehran Tehran Iran

ISBN: (纸本)9798331511272

This paper reviews the evolution of natural language processing (NLP) models, concentrating on the distillation techniques used to create efficient and compact versions of large models. Traditional NLP models laid the foundation but had limitations in scalability and contextual understanding. Transformer models like BERT revolutionized NLP but required significant computational resources. This review examines TinyBERT, DistilBERT, MobileBERT, and MiniLM, which balance size and performance through knowledge distillation. These distilled models deliver robust performance while being optimized for use on devices with limited computational resources, enabling the application of advanced NLP features in practical scenarios. ©2024 IEEE.

关键词： natural language processing systems

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Research on Computer Intelligent ChatGPT natural language processing System Based on Scientific knowledge Graph 24

Research on Computer Intelligent ChatGPT Natural Language Pr...

引用

international conference on Machine Intelligence and Digital Applications (MIDA)

作者： Zhan, Liuchun Huang, Changjiang Guangzhou Coll Appl Sci & Technol Coll Comp Sci Guangzhou Peoples R China Guangzhou Coll Appl Sci & Technol Res Base Guangdong Social Sci Federat Urban & Rur Guangzhou Peoples R China

ISBN: (纸本)9798400718144

A synonym mining method is proposed by combining the character vector graph and noise robust learning method. The model uses paired word vectors pre-trained by ChatGPT to enhance entity semantic representation. Classify marks with noise. Then the cross optimal processing is carried out to identify the true and false marks. The two-layer construction system of knowledge extraction and knowledge fusion is constructed to realize the independent construction and answer of software engineering questions. The system effectively improves the efficiency of software project understanding and software reuse.

关键词： Software knowledge graph ChatGPT Software knowledge extraction natural language processing system

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Providing a natural language processing App for language Teachers 26th

Providing a Natural Language Processing App for Language Tea...

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26th international conference on Interactive Collaborative Learning (ICL) - Towards a Hybrid, Flexible and Socially Engaged Higher Education / 52nd IGIP international conference on engineering Pedagogy

作者： Posekany, Alexandra Dolezal, Dominik TU Vienna Univ Technol Vienna Austria TGM Vienna Inst Technol Vienna Austria Univ Vienna Vienna Austria

ISBN: (纸本)9783031519789;9783031519796

natural language processing (NLP) is a common application for Artificial Intelligence. The goal is to provide language teachers with a simple to apply tool for topic model analyses to integrate into their classroom. The project also involves project based learning for students programming the actual AI web application. The original notion is to provide language teacher with AI methodology without requiring any technical knowledge in AI or any programming skills. natural language processing provides various tools for word frequencies, but also topic modelling, allowing to track the relevance of topics over time in the media or in the literature. In collaboration with University of Technology linguistics, we intend to provide a corpus of classical English and German literature, as well as the option of uploading your own corpus which can be obtained from webscraping or other sources. A team of students of the vocational high school TGM Wien specialised in IT and Software Development is working on the design of the interactive GUI for this NLP application, learning in this way the methods of natural language processing and Artrificial Intelligence in a project based setting. For this the statistical programming language R is utilized which already provides packages with implementation for natural language processing and in addition the shiny package which allows to develop interactie web apps without additional web and app programming. A team of teachers supervises and supports the students during the development process, providing expertise in AI and NLP, in web and app programming, as well as server management. Two intended outcomes exist. Ont the one hand, we want our students to learn natural language processing first hand through development of this application. On the other hand, we intend to obtain an interactive AI tool which can assist language teachers and their students on the long term in the classroom. In times of GPT3 and GPT4 dominating the media and per

关键词： Artificial intelligence natural language processing Interactive web application Project based learning Software development

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AcawebAgent: A Large language Model-Powered Assistant for Early Academic Research 5

AcawebAgent: A Large Language Model-Powered Assistant for Ea...

引用

5th international conference on Computer engineering and Application (ICCEA)

作者： Yang, Yingli Wang, Xiaoting Univ Elect Sci & Technol China Inst Fundamental & Frontier Sci Chengdu Peoples R China

ISBN: (纸本)9798350386783;9798350386776

The rapid advancement of artificial intelligence (AI) and machine learning (ML) technologies, particularly in large language models (LLMs) like the GPT series, has significantly impacted research and industrial applications. These models excel in various natural language processing (NLP) tasks, including text generation, comprehension, and translation. However, harnessing these capabilities for academic research still presents challenges, particularly for early-career researchers navigating extensive literature. In this paper, we introduce AcawebAgent, an inventive AutoAgent specifically crafted to enhance the abilities of beginner researchers. It leverages the advanced generation and analysis capabilities of large language models (LLMs) to collect open academic knowledge from the web. AcawebAgent offers customized research reports that include in-depth overviews, practical applications, the latest developments, and future trajectories of specific research domains, thereby significantly diminishing the time and effort needed for comprehensive literature reviews and trend analyses.

关键词： component Large language models AutoAgent Web search Early academic research

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Evaluating Class Membership Relations in knowledge Graphs Using Large language Models

Evaluating Class Membership Relations in Knowledge Graphs Us...

引用

21st international conference on The Semantic Web (ESWC)

作者： Allen, Bradley P. Groth, Paul T. Univ Amsterdam Amsterdam Netherlands

ISBN: (纸本)9783031789519;9783031789526

A backbone of knowledge graphs are their class membership relations, which assign entities to a given class. As part of the knowledge engineering process, we propose a new method for evaluating the quality of these relations by processing descriptions of a given entity and class using a zero-shot chain-of-thought classifier that uses a natural language intensional definition of a class. We evaluate the method using two publicly available knowledge graphs, Wikidata and CaLiGraph, and 7 large language models. Using the gpt-4-0125-preview large language model, the method's classification performance achieves a macro-averaged F1-score of 0.830 on data from Wikidata and 0.893 on data from CaLiGraph. Moreover, a manual analysis of the classification errors shows that 40.9% of errors were due to the knowledge graphs, with 16.0% due to missing relations and 24.9% due to incorrectly asserted relations. These results show how large language models can assist knowledge engineers in the process of knowledge graph refinement. The code and data are available on Github (https://***/bradleypallen/evaluating-kg-class-memberships-using-llms).

关键词： knowledge engineering large language models knowledge graph refinement natural language generation

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Automatic Prompt Generation Based on Combinatorial Relationships in Reasoning Problems 6

Automatic Prompt Generation Based on Combinatorial Relations...

引用

6th international conference on natural language processing (ICNLP)

作者： Wu, Tong He, Hao Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn State Key Lab Adv Opt Commun Syst & Network Shanghai Peoples R China

ISBN: (纸本)9798350349122;9798350349115

The effectiveness of Large language Models (LLMs) in tasks involving reasoning is significantly influenced by the structure and formulation of the prompts, contemporary research in prompt engineering aims to help LLMs better understand the paradigms of reasoning question (e.g., CoT). However, these efforts have either struggled to effectively incorporate external knowledge into single prompt or integrating entire corpus information, often fails to significantly enhance the reasoning capabilities of LLMs. This paper introduces a novel prompting method that incorporates implicit hints that represent logical combinatorial relationships between known conditions in reasoning problems, guiding LLMs to think correctly in the initial steps of reasoning for such problems. Extensive and comprehensive experiment results on four different reasoning problem datasets indicate that our proposed method improved accuracy while maintaining efficiency.

关键词： Large language Models Prompt engineering natural language Reasoning

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Advancing Capability Matching in Manufacturing Reconfiguration with Large language Models 1

引用

33rd international conference on Flexible Automation and Intelligent Manufacturing

作者： Mo, Fan Chaplin, Jack C. Sanderson, David Ratchev, Svetan Univ Nottingham Inst Adv Mfg Nottingham NG8 England Univ Nottingham Ctr Aerosp Mfg Nottingham NG7 2GX Notts England

ISBN: (数字)9783031744853

ISBN: (纸本)9783031744846;9783031744853

This paper introduces an approach that integrates natural language processing (NLP) and knowledge graphs with Reconfigurable Manufacturing Systems (RMS) to enhance flexibility and adaptability. We utilize a chatbot interface powered by GPT-4 and a structured knowledge base to simplify the complexities of manufacturing reconfiguration. This system not only boosts reconfiguration efficiency but also broadens accessibility to advanced manufacturing technologies. We demonstrate our methodology through an application in capability matching, showcasing how it facilitates the identification of assets for new product requirements. Our results indicate that this integrated solution offers a scalable and user-friendly approach to overcoming adaptability challenges in modern manufacturing environments.

关键词： natural language processing chatbot large language model manufacturing reconfiguration knowledge graph

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Can LLaMA 3 Understand Monetary Policy? 17

Can LLaMA 3 Understand Monetary Policy?

引用

17th international conference on Advanced Computer Theory and engineering

作者： Wu, Yingyi Xiao, Zhaomin Zhang, Jinran Mai, Zhelu Xu, Zhuoer Meta Menlo Pk CA 94025 USA Univ North Texas Denton TX 76203 USA I 66 Express Mobil Partners Manassas VA USA Hewlett Packard Enterprise Houston TX USA

ISBN: (纸本)9798331539894;9798331539887

This study provides an in-depth examination of LLaMA 3's performance on a domain-specific task, specifically classifying monetary policy texts. The aim is to categorize these texts as hawkish, dovish, or neutral by using a prompt to assess LLaMA 3's understanding of finance-related knowledge anticipated to be acquired during its pretraining. Experimental results demonstrate that LLaMA 3 has indeed developed the necessary commonsense knowledge to interpret monetary policy, as it surpasses both major and random baselines. To further understand its performance, we also analyze the consistency of its impressive results across different model inputs, including the number of tokens, nouns, and verbs.

关键词： natural language processing large language models finance monetary policy

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Construction of Intelligent Educational Teaching Evaluation System Based on knowledge Graph and natural language processing 2

Construction of Intelligent Educational Teaching Evaluation ...

引用

2nd IEEE international conference on Sensors, Electronics and Computer engineering, ICSECE 2024

作者： Lin, Lin Shandong Vocational College of Science and Technology Weifang China

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373646

This project constructs a subject knowledge map for instructional design based on natural language processing technology. This provides a new way of thinking and method for the teaching practice of this subject. Firstly, the ontology-oriented dictionary learning method is studied. By using the fusion ontology relation extraction method, the subject-action-semantic association is extracted to enrich the category of association. Ontology alignment technique is used to disambiguate the extracted knowledge base. Using 'Neo4j' as the development tool, the knowledge map of course design theme is displayed visually. It is found that presenting the image of information and knowledge in the subject teaching is an operable technological support. The system further expands the applied research field. knowledge graph and other visual tools will be used in more education fields in the future to achieve specific visual education based on the corresponding experience and lessons. © 2024 IEEE.

关键词： knowledge graph

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