检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

26,456 篇 会议
407 册 图书
372 篇 期刊文献
3 篇 学位论文
1 篇 资讯

馆藏范围

27,239 篇 电子文献
2 种 纸本馆藏

日期分布

学科分类号

16,518 篇 工学
- 15,126 篇 计算机科学与技术...
- 11,292 篇 软件工程
- 2,789 篇 信息与通信工程
- 1,774 篇 控制科学与工程
- 1,559 篇 电气工程
- 1,411 篇 生物工程
- 626 篇 生物医学工程（可授...
- 610 篇 化学工程与技术
- 556 篇 机械工程
- 514 篇 电子科学与技术（可...
- 476 篇 网络空间安全
- 397 篇 仪器科学与技术
- 375 篇 光学工程
- 280 篇 安全科学与工程
5,082 篇 理学
- 2,073 篇 数学
- 1,471 篇 物理学
- 1,470 篇 生物学
- 786 篇 统计学（可授理学、...
- 620 篇 系统科学
- 617 篇 化学
4,728 篇 管理学
- 3,169 篇 图书情报与档案管...
- 1,838 篇 管理科学与工程(可...
- 773 篇 工商管理
839 篇 医学
- 611 篇 临床医学
- 569 篇 基础医学(可授医学...
- 393 篇 公共卫生与预防医...
- 250 篇 药学(可授医学、理...
684 篇 法学
- 602 篇 社会学
573 篇 文学
- 401 篇 外国语言文学
454 篇 教育学
- 443 篇 教育学
252 篇 经济学
177 篇 农学
19 篇 艺术学
17 篇 军事学
1 篇 哲学

主题

5,691 篇 natural language...
1,726 篇 natural language...
1,552 篇 semantics
1,445 篇 natural language...
1,195 篇 sentiment analys...
1,192 篇 machine learning
1,147 篇 deep learning
978 篇 accuracy
917 篇 training
809 篇 data mining
797 篇 feature extracti...
795 篇 computational mo...
772 篇 artificial intel...
544 篇 knowledge engine...
523 篇 speech recogniti...
514 篇 transformers
512 篇 large language m...
492 篇 data models
464 篇 information retr...
463 篇 support vector m...

机构

58 篇 chitkara univers...
51 篇 lovely professio...
49 篇 school of comput...
33 篇 university of ch...
31 篇 school of comput...
31 篇 centre of interd...
31 篇 xi'an university...
30 篇 school of comput...
28 篇 tsinghua univers...
28 篇 school of comput...
27 篇 school of inform...
26 篇 zhejiang univers...
26 篇 microsoft resear...
24 篇 institute of inf...
24 篇 school of comput...
23 篇 school of inform...
22 篇 department of co...
22 篇 beijing univ pos...
21 篇 college of compu...
21 篇 school of inform...

作者

29 篇 liu yang
27 篇 wan wanggen
20 篇 tiejun zhao
18 篇 zhou guodong
18 篇 wang lei
16 篇 zhang tao
16 篇 zhao hai
16 篇 yang yang
15 篇 sabetzadeh mehrd...
15 篇 lei li
15 篇 liu ying
14 篇 li sheng
14 篇 wu jin
14 篇 zhang lei
14 篇 liu jun
13 篇 li yang
13 篇 li li
13 篇 shanmugasundaram...
13 篇 f. ren
12 篇 li peng

语言

26,264 篇 英文
874 篇 其他
151 篇 中文
5 篇 土耳其文
2 篇 西班牙文
1 篇 法文
1 篇 葡萄牙文

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

共 27241 条记录，以下是471-480 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

A comprehensive analysis and sensitivity model for emotion detection in text 1st

A comprehensive analysis and sensitivity model for emotion d...

引用

1st international conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Singh, Pancham Asthana, Amita Solanki, Beerbal Tomar, Swati Kansal, Mrignainy Tyagi, Gagan Department of Information Technology Ajay Kumar Garg Engineering College Ghaziabad India Department of Electronics and Communication Engineering Ajay Kumar Garg Engineering College Ghaziabad India Department of Computer Science and Engineering Ajay Kumar Garg Engineering College Ghaziabad India Technical Architect Tech Mahindra India

ISBN: (纸本)9781032874036

People use a variety of communication channels, including text, voice, and gestures, to convey a wide range of emotions in their daily interactions. The problem of automatically identifying these emotions from textual information has important practical ramifications. This study focuses on natural language processing (NLP) methods for textual emotion identification, particularly in Twitter tweets. The study uses machine learning, sentiment analysis, tokenization, and other techniques with a focus on using APIs such as the Twitter API. With an emphasis on the use of unsupervised learning algorithms and data expansion methodologies, the focal issue examines relevant work in sentiment analysis on social media. The suggested approach tackles issues including tokenization, sentiment analysis, and noise reduction in order to categorize tweets into distinct moods. The front-end development using Flask provides a user-friendly interface to visualize real-time Twitter data based on user-defined keywords. The study achieved an overall accuracy rate of 92% in classifying human moods across a diverse dataset. © 2025 the Author(s).

关键词： Tweets

来源：评论

学校读者我要写书评

暂无评论

LegalAI Across languages: A Comprehensive Survey on Tasks, Methodologies and Challenges 1

LegalAI Across Languages: A Comprehensive Survey on Tasks, M...

引用

1st international conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Deshpande, Anjali Deshmukh, Pratiksha COEP Technological University Dept. of Computer Science and Engineering Pune India PICT Dept. of AI & DS Pune India School of Computational Sciences COEP Technological University Dept. of Computer Science and Engineering Pune India

ISBN: (纸本)9798350355611

natural language processing (NLP) is revolutionizing the legal domain, enabling tasks such as predicting legal outcomes, summarizing complex documents, identifying key entities, and assessing bail risks. This survey paper presents a comprehensive review of the existing research on NLP tasks applied to legal documents and judgments across various languages, with a special emphasis on low-resource languages. Despite the extensive work done in widely spoken languages like English, there remains a critical need to extend these advancements to languages with limited resources and data availability. This paper categorizes and analyzes the NLP tasks in the legal domain based on the languages they target. It highlights the methodologies employed, datasets utilized, and the performance of different models across diverse languages. Furthermore, it identifies the gaps in current research and proposes potential directions for future work, particularly in improving the accessibility and applicability of NLP solutions for legal texts in underrepresented languages. By providing a comprehensive overview of advancements and obstacles, this survey seeks to assist future research endeavors that bridge the disparity between high-resource and low-resource languages in legalAI applications. © 2025 IEEE.

关键词： Risk assessment

来源：评论

学校读者我要写书评

暂无评论

NeOn-GPT: A Large language Model-Powered Pipeline for Ontology Learning

NeOn-GPT: A Large Language Model-Powered Pipeline for Ontolo...

引用

21st international conference on The Semantic Web (ESWC)

作者： Fathallah, Nadeen Das, Arunav De Giorgis, Stefano Poltronieri, Andrea Haase, Peter Kovriguina, Liubov Univ Stuttgart Inst Artificial Intelligence Analyt Comp Stuttgart Germany Kings Coll London London England Univ Bologna Inst Cognit Sci & Technol Natl Res Council ISTC C Bologna Italy Univ Bologna Dept Comp Sci & Engn Bologna Italy Metaphacts GmbH Walldorf Germany

ISBN: (纸本)9783031789519;9783031789526

We address the task of ontology learning by combining the structured NeOn methodology framework with Large language Models (LLMs) for translating natural language domain descriptions into Turtle syntax ontologies. The main contribution of the paper is a prompt pipeline tailored for domain-agnostic modeling, exemplified through the application to a domain-specific case study: the wine ontology. The resulting pipeline is used to develop NeOn-GPT, a workflow for automatic ontology modeling, and its proof of concept implementation, integrated on top of the metaphactory platform. NeOn-GPT leverages the systematic approach of the NeOn methodology and LLMs' generative capabilities to facilitate a more efficient ontology development process. We evaluate the proposed approach by conducting comprehensive evaluations using the Stanford wine ontology as the gold standard. The obtained results show, that LLMs are not fully equipped to perform procedural tasks required for ontology development, and lack the reasoning skills and domain expertise needed. Overall, LLMs require integration with the workflow or trajectory tools for continuous knowledge engineering tasks. Nevertheless, LLMs can significantly alleviate the time and expertise needed. Our code base is publicly available for research and development purposes, accessible at: https://***/andreamust/NEON-GPT.

关键词： Ontology Modelling Large language Models NeOn Methodology

来源：评论

学校读者我要写书评

暂无评论

A Review on Leveraging language Agnostic Automated Text Data Augmentation with Explainable Semantic Integrity 5

A Review on Leveraging Language Agnostic Automated Text Data...

引用

5th international conference on Advanced Research in Computing, ICARC 2025

作者： Wijesundera, Sarith Peiris, Suresh Michael University of Westminster Department of Computer Science and Engineering London United Kingdom Informatics Institute of Technology School of Computing Colombo Sri Lanka

ISBN: (纸本)9798331530983

Data Augmentation (DA) is an effective strategy to increase model generalisation. In natural language processing (NLP), DA remains in its early stages, primarily due to the inherent sensitivity of textual data, which complicates the augmentation process. These complexities are further increased in multilingual contexts, as DA techniques effective in one language may not yield comparable results in others. As a result, manually identifying optimal DA techniques that preserve semantic integrity and are language agnostic remains a significant challenge. It emphasises the necessity for automating the text DA process with Explainable Artificial Intelligence (XAI), which offers a promising approach by calculating word importance scores to effectively guide the augmentation. In this paper, a comprehensive review of existing approaches in auto-text DA is presented, beginning with an exploration of the concept of auto-text DA, followed by a discussion on its cross-lingual applications, and concluding with the integration of XAI for semantic preservation. The paper highlights the critical need for further research to enhance the effectiveness and applicability of XAI in auto-text DA, enabling its use across diverse languages. © 2025 IEEE.

关键词： Transfer learning

来源：评论

学校读者我要写书评

暂无评论

Multimodal Learning in natural language processing: Fact-Checking, Ethics and Misinformation Detection

Multimodal Learning in Natural Language Processing: Fact-Che...

引用

Multi-Agent Systems for Collaborative Intelligence (ICMSCI), international conference on

作者： Himanshu Sanjay Joshi Hamed Taherdoost College of Business Westcliff University CA USA R&D Department Hamta Business Corporation Vancouver Canada College of Technology & Engineering Westcliff University CA USA University Canada West Vancouver Canada GUS Institute | Global University Systems London UK Q Minded Quark Minded Technology Inc. Vancouver Canada

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Multimodal learning (MML) is a subtype of deep learning that improves the capabilities of machine learning models by integrating modalities from multiple data sources such as text, images, audio, and video. This integration of modalities produces a more comprehensive understanding of complex data and nuanced outputs. Applications of this approach in real-time decision-making, information retrieval, and knowledge representation can improve the insights derived from the natural language processing (NLP) system. Moreover, in a time of information overload, there are concerns regarding the credibility of the content. The role of NLP systems is central to processing textual data in MML systems. In this environment, the ethical development of multimodal systems for fact-checking is crucial. This paper addresses an existing gap in use of multimodal learning in the context of fact-checking by investigating issues related to integration of NLP, Question-Answering (QA) systems, and knowledge graphs to combat misinformation. This research presents multimodal data based frameworks based on knowledge graph integration. The exploration of the frameworks enables to enhance the accuracy, reliability, and ethical soundness of fact-checking mechanisms. This study also highlights new directions for overcoming challenges related to modality alignment, computational efficiency, and ethical transparency.

关键词： Ethics Technological innovation Accuracy Soft sensors Scalability knowledge graphs Streaming media Reliability theory natural language processing Fake news

来源：评论

学校读者我要写书评

暂无评论

Teach me : AI-Powered Voice Chat Robot Using Internet of Things 5

Teach me : AI-Powered Voice Chat Robot Using Internet of Thi...

引用

5th international conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Kishor Babu, B.R. Rajasivam, A. Suresh, R. Kalai Selvi, T. Meena, R. Easwari Engineering College Chennai India

ISBN: (纸本)9798331522100

This paper presents TEACH ME: An Artificial Intelligence-Powered Voice Chat Robot Integrated with the Internet of Things (IoT), designed to facilitate flexible, voice-powered communication with connected devices Leveraging Large language Models (LLMs). TEACH ME natural language processing Increases (NLP) Powerful, robust robotic voice translation enables them to create, engage in active conversations, and respond in human-like ways that integrate LLMs with the IoT extend beyond traditional systems for experiencing it is highly personalized and contextual, and allows users to interact with smart devices This system design seamlessly combines the capabilities of LLMs with IoT devices, 1999 - creating new opportunities for applications in areas such as smart home automation, education, customer support, etc. TEACH ME's key components include real-time voice recognition, advanced response generation, and IoT-powered task performance. Examines applicability and future directions. © 2025 IEEE.

关键词： Educational robots

来源：评论

学校读者我要写书评

暂无评论

InVideo Search: Scene Description Clustering and Integrating Image and Audio Captioning for Enhanced Video Search 21st

InVideo Search: Scene Description Clustering and Integrating...

引用

21st international conference on Distributed Computing and Intelligent Technology

作者： Khan, Almira Asif Muhammed Shaji, Asher Mathews Sujith, Devika Nath, Aneesh G. Udmale, Sandeep S. TKM Coll Engn Dept Comp Sci & Engn Kollam Kerala India Veermata Jijabai Technol Inst VJTI Dept Comp Engn & Informat Technol Mumbai 400019 Maharashtra India

ISBN: (纸本)9783031814037;9783031814044

In the digital era, the vast amount of long videos highlights the need for an advanced system to efficiently find specific segments based on text descriptions. The proposed solution aims to develop a video content retrieval system by utilizing established tools in video analysis, image/video captioning, and information retrieval. The work is organized into three modules: Keyframe extraction, Captioning, and Query-based searching. Our primary focus is on effectively identifying key elements within these videos, achieving a semantic understanding of these videos in the form of captions, and establishing precise matches of user queries. In short, users input descriptive text prompts, and the application, equipped with pre-trained models, promptly and accurately identifies and retrieves the video segments. This application carries significant potential across various domains, from enabling efficient scene location in movies to helping extract knowledge from educational lectures. Furthermore, this research fundamentally departs from conventional video search methods that heavily rely on metadata or manually assigned tags, which often fall short of capturing the nuances of video content. This work also focuses on a multimodal captioning strategy to integrate more semantic content. Clustering of captions was also incorporated to improve search performance in the case of long videos. This approach allows us to deliver a robust and efficient solution, taking advantage of the extensive knowledge and research underlying these established technologies. Beyond the immediate applications, this research has broader applications in fields like natural language understanding, multimedia analysis, and information retrieval.

关键词： Clustering Keyframe extraction natural language processing Video content retrieval system

来源：评论

学校读者我要写书评

暂无评论

Transliteration Framework for Tamil-English Code-Mixed Text

Transliteration Framework for Tamil-English Code-Mixed Text

引用

international conference on Business Intelligence and Data Analytics, BIDA 2024

作者： Viswabharathi, K.A. Rithick, K.R. Thayumanan, T. Nalinadevi, K. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9789819777167

Code-mixing is a common phenomenon in multilingual communities, where speakers use more than one language within a single conversation. A transliteration framework is necessary to accommodate individuals who feel more comfortable primarily using their native tongues for communication in the realm of social media. The proposed framework leverages existing transliteration techniques and incorporates novel approaches to handle the challenges specific to English-Tamil code-mixed data. The word-level language classification was implemented using supervised machine learning models and HMM, with the latter performing better with an accuracy of 75%. Tamil-tagged words from the classifier are passed to the Seq2seq encoder-decoder model for transliteration, and the model achieved an accuracy of 93%. The effectiveness of the framework is evaluated using a corpus of code-mixed data, and the results are compared with existing transliteration methods, demonstrating its efficacy in transliterating code-mixed data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

Enhancing Information Extraction from Building Standards with ChatGPT-4: A Multimodal Approach 20th

Enhancing Information Extraction from Building Standards wit...

引用

20th international conference on Computing in Civil and Building engineering, ICCCBE 2024

作者： Zentgraf, Sven König, Markus Chair of Computing in Engineering Ruhr-Universität Bochum Universitätsstraße 150 Bochum44801 Germany

ISBN: (纸本)9783031842078

The AECO (Architectural, engineering, Construction, and Operations) industry is increasingly benefiting from the advantages of advanced information management, particularly through the use of Building Information Modeling (BIM) methods. BIM integrates geometric and alphanumeric data, such as dimensions and spatial relationships, into digital building models. These elements are subject to specific constraints defined by industry standards and guidelines. The issue with these constraints embedded in regulatory documents is that they are mostly only available in non-machine readable formats which hinders the exchange and usage of data within the building’s lifecycle. Currently, the required rules and requirements are extracted manually by experts from the regulatory documents and integrated manually and labor-intensively into corresponding BIM processes. Thus, many research projects are currently addressing the issue of how the textual components of regulatory documents can be automatically analyzed to extract rules and requirements using natural language processing or other approaches. In addition to this textual information, many standards also contain figures and illustrations that explain the information described in the text in more detail or contain additional knowledge. Converting this graphical content into machine-readable information is just as challenging as analyzing the textual components. This paper aims to develop an approach for extracting information related to building information requirements from figures contained in standards using the two state-of-the-art MLLMs Kosmos 2 and Chat GPT-4. An MLLM is an AI system that can process text, images, audio, and video, unlike traditional models that are limited to text. MLLMs trained on large data sets recognize complex patterns in different types of data. With the help of the two MLLMs, the images, and their corresponding texts and captions from the standards will first be analyzed, and then the information

关键词： Concretes

来源：评论

学校读者我要写书评

暂无评论

Evaluating the Contribution of AI-Powered knowledge and Digital Literacy to the Improvement of Fintech Operations in the Electronics Sector 1

Evaluating the Contribution of AI-Powered Knowledge and Digi...

引用

1st international conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Choudhari, Amey Adinath Moholkar, Nilambari Hanmant Ghadage, Avinash Bhoite, Dashrath Kumari, Sujata Patil, Mangalgouri S JSPM's Rajashi Shahu College of Engineering Pune India Dr. D.Y. Patil Institute of Technology Pimpri Pune India D.Y. Patil Vidyapeeth's Global Business School and Research Centre Pune India International School of Management and Research Pune India Shri Balaji University Pune India Pune India

ISBN: (纸本)9798331518578

The financial services sector is undergoing a change because to artificial intelligence (AI) particularly in the field of financial technology (FinTech). FinTech's incorporation of artificial intelligence (AI) has fundamentally altered traditional financial services by increasing security, accuracy, and efficiency. Artificial intelligence (AI) technology, financial organizations can benefit from technologies like ML algorithms, natural language processing (NLP), including predictive analytics in making better decisions, streamlining operations, and offering customized customer experiences. While cutting edge capability and advanced education enables both staff and purchasers to capably connect with fintech courses of action, recreated insight-controlled data helps associations with overhauling heading, further foster client experiences, and limit money related risks. Since technology has made it easier to share information and lower transaction costs, financial services production could be broken down. Because specialized players offer unbundled financial services, customers can locate and put together the product suites that they want. This study sees how cutting-edge capability and man-made brainpower-controlled data could chip away at financial undertakings, advance safe trades, and addition proficiency in the contraptions business. This study assesses how these advances add to the augmentation and goodness of fintech applications in equipment by dissecting context-oriented examinations, ongoing crazes, and inconveniences. © 2025 IEEE.

关键词： Sales

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共500页 << < 44 45 46 47 48 49 50 51 52 53 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：