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

14,303 篇 会议
98 册 图书
66 篇 期刊文献
1 篇 资讯

馆藏范围

14,468 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

9,574 篇 工学
- 8,758 篇 计算机科学与技术...
- 6,972 篇 软件工程
- 2,028 篇 信息与通信工程
- 1,454 篇 控制科学与工程
- 1,256 篇 电气工程
- 907 篇 生物工程
- 473 篇 网络空间安全
- 436 篇 电子科学与技术（可...
- 421 篇 生物医学工程（可授...
- 313 篇 化学工程与技术
- 307 篇 仪器科学与技术
- 294 篇 机械工程
- 268 篇 光学工程
- 256 篇 安全科学与工程
- 240 篇 交通运输工程
- 182 篇 动力工程及工程热...
3,191 篇 理学
- 1,265 篇 数学
- 932 篇 生物学
- 900 篇 物理学
- 503 篇 统计学（可授理学、...
- 462 篇 系统科学
- 378 篇 化学
2,915 篇 管理学
- 1,865 篇 图书情报与档案管...
- 1,281 篇 管理科学与工程(可...
- 472 篇 工商管理
613 篇 医学
- 418 篇 临床医学
- 369 篇 基础医学(可授医学...
- 353 篇 公共卫生与预防医...
470 篇 法学
- 438 篇 社会学
229 篇 教育学
- 225 篇 教育学
207 篇 文学
159 篇 经济学
98 篇 农学
11 篇 艺术学
9 篇 军事学
1 篇 哲学

主题

2,963 篇 natural language...
874 篇 natural language...
839 篇 natural language...
817 篇 semantics
577 篇 deep learning
554 篇 machine learning
533 篇 sentiment analys...
507 篇 data mining
395 篇 knowledge engine...
368 篇 large language m...
362 篇 training
355 篇 computational mo...
312 篇 feature extracti...
309 篇 speech recogniti...
304 篇 speech processin...
275 篇 signal processin...
272 篇 accuracy
241 篇 neural networks
239 篇 transformers
237 篇 knowledge based ...

机构

57 篇 institute of inf...
52 篇 school of cyber ...
49 篇 school of commun...
40 篇 chitkara univers...
40 篇 maharishi school...
35 篇 lovely professio...
34 篇 university of ch...
34 篇 school of comput...
33 篇 school of comput...
31 篇 xi'an university...
30 篇 faculty of engin...
27 篇 department of el...
23 篇 school of inform...
22 篇 school of comput...
22 篇 department of co...
20 篇 centre of interd...
19 篇 beijing universi...
18 篇 pune
17 篇 zhejiang univers...
17 篇 college of compu...

作者

38 篇 ren fuji
27 篇 wan wanggen
17 篇 liu yang
16 篇 yixin zhong
14 篇 wu jin
14 篇 lei li
14 篇 shingo kuroiwa
13 篇 zhang tao
13 篇 wang lei
13 篇 sabetzadeh mehrd...
13 篇 zheng dequan
13 篇 liu jie
12 篇 tiejun zhao
12 篇 f. ren
12 篇 li ying
11 篇 li sheng
11 篇 yang yang
11 篇 tao zhang
11 篇 arora chetan
11 篇 wang wei

语言

10,918 篇 英文
3,521 篇 其他
53 篇 中文
1 篇 土耳其文

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

共 14468 条记录，以下是21-30 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：

GPTZoo: A Large-scale Dataset of GPTs for the Research Community 24

GPTZoo: A Large-scale Dataset of GPTs for the Research Commu...

引用

39th ACM/ieee international conference on Automated Software engineering (ASE)

作者： Hou, Xinyi Zhao, Yanjie Wang, Shenao Wang, Haoyu Huazhong Univ Sci & Technol Hubei Engn Res Ctr Big Data Secur Sch Cyber Sci & Engn Hubei Key Lab Distributed Syst Secur Wuhan Peoples R China

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

The rapid advancements in Large language Models (LLMs) have revolutionized natural language processing, with GPTs, customized versions of ChatGPT available on the GPT Store, emerging as a prominent technology for specific domains and tasks. To support academic research on GPTs, we introduce GPTZoo, a large-scale dataset comprising 730,420 GPT instances. Each instance includes rich metadata with 21 attributes describing its characteristics, as well as instructions, knowledge files, and third-party services utilized during its development. GPTZoo aims to provide researchers with a comprehensive and readily available resource to study the real-world applications, performance, and potential of GPTs. To facilitate efficient retrieval and analysis of GPTs, we also developed an automated command-line interface (CLI) that supports keyword-based searching of the dataset. To promote open research and innovation, the GPTZoo dataset will undergo continuous updates, and we are granting researchers public access to GPTZoo and its associated tools.

关键词： Large language Model LLM ChatGPT GPTs

来源：评论

学校读者我要写书评

暂无评论

Multi-Perspective Transfer Learning for Automatic MOS Prediction of Low Resource language

Multi-Perspective Transfer Learning for Automatic MOS Predic...

引用

28th international conference on Asian language processing (IALP)

作者： Yin, Pengkai Liu, Rui Bao, Feilong Gao, Guanglai Inner Mongolia Univ Coll Comp Sci Hohhot Peoples R China

ISBN: (纸本)9798331540869;9798331540852

Mean Opinion Score (MOS) prediction is the task to automatically evaluate synthesized speech by a neural network that emulates a human listening test. Traditional automatic MOS prediction typically focused on mainstream languages, such as English, due to large available data. However, for low-resource languages, there is no large-scale MOS prediction data, that hinders the study of those languages. In this paper, we propose a novel Multi-Perspective Transfer Learning (MPTL) training scheme with a new small-scale Mongolian MOS prediction dataset MonMOS. MPTL includes Feature Transfer and Model Transfer to transfer knowledge from the mainstream languages to low-resource language from different perspectives. The experimental results on the MonMOS show that the MPTL outperforms the standard direct training scheme with classical architecture. We will release the pre-trained models and MonMOS dataset at: https://***/Ai-S2-Lab/MPTL-MOS.

关键词： MOS Prediction Low-Resource language Transfer Learning

来源：评论

学校读者我要写书评

暂无评论

Lessons from the Use of natural language Inference (NLI) in Requirements engineering Tasks 32

Lessons from the Use of Natural Language Inference (NLI) in ...

引用

32nd ieee international Requirements engineering conference (RE)

作者： Fazelnia, Mohamad Koscinski, Viktoria Herzog, Spencer Mirakhorli, Mehdi Univ Hawaii Manoa Dept Comp Sci Honolulu HI 96822 USA Rochester Inst Technol Global Cybersecur Inst Rochester NY 14623 USA

ISBN: (纸本)9798350395129;9798350395112

We investigate the use of natural language Inference (NLI) in automating requirements engineering tasks. In particular, we focus on three tasks: requirements classification, identification of requirements specification defects, and detection of conflicts in stakeholders' requirements. While previous research has demonstrated significant benefit in using NLI as a universal method for a broad spectrum of natural language processing tasks, these advantages have not been investigated within the context of software requirements engineering. Therefore, we design experiments to evaluate the use of NLI in requirements analysis. We compare the performance of NLI with a spectrum of approaches, including prompt-based models, conventional transfer learning, Large language Models (LLMs)-powered chatbot models, and probabilistic models. Through experiments conducted under various learning settings including conventional learning and zero-shot, we demonstrate conclusively that our NLI method surpasses classical NLP methods as well as other LLMs-based and chatbot models in the analysis of requirements specifications. Additionally, we share lessons learned characterizing the learning settings that make NLI a suitable approach for automating requirements engineering tasks.

关键词： Requirements engineering natural language Inference Large language Models Software Requirements Classification Specification Defects Requirements Conflict Detection

来源：评论

学校读者我要写书评

暂无评论

KENET:knowledge-ENHANCED DOC-LABEL ATTENTION NETWORK FOR MULTI-LABEL TEXT CLASSIFICATION 49

KENET:KNOWLEDGE-ENHANCED DOC-LABEL ATTENTION NETWORK FOR MUL...

引用

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

作者： Li, Bo Chen, Yuyan Zeng, Liang Baidu Inc Beijing Peoples R China Fudan Univ Shanghai Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Multi-Label Text Classification (MLTC) is a fundamental task in the field of natural language processing (NLP) that involves the assignment of multiple labels to a given text. MLTC has gained significant importance and has been widely applied in various domains such as topic recognition, recommendation systems, sentiment analysis, and information retrieval. However, traditional machine learning and Deep neural network have not yet addressed certain issues, such as the fact that some documents are brief but have a large number of labels and how to establish relationships between the labels. It is imperative to additionally acknowledge that the significance of knowledge is substantiated in the realm of MLTC. To address this issue, we provide a novel approach known as knowledge-enhanced Doc-Label Attention Network (KeNet). Specifically, we design an Attention Network that incorporates external knowledge, label embedding, and a comprehensive attention mechanism. In contrast to conventional methods, we use comprehensive representation of documents, knowledge and labels to predict all labels for each single text. Our approach has been validated by comprehensive research conducted on three multi-label datasets. Experimental results demonstrate that our method outperforms state-of-the-art MLTC method. Additionally, a case study is undertaken to illustrate the practical implementation of KeNet.

关键词： Multi-Label Text Classification natural language processing

来源：评论

学校读者我要写书评

暂无评论

COQ:AN EMPIRICAL FRAMEWORK FOR MULTI-HOP QUESTION ANSWERING EMPOWERED BY LARGE language MODELS 49

COQ:AN EMPIRICAL FRAMEWORK FOR MULTI-HOP QUESTION ANSWERING ...

引用

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

作者： Huang, Qiang Huang, Feng Tao, DeHao Zhao, YueTong Wang, BingKun Huang, YongFeng Xinjiang Univ Coll Informat Sci & Engn Urumqi Peoples R China Tsinghua Univ Dept Elect Engn Beijing Peoples R China Zhengzhou Coll Finance & Econ Sch Informat Engn Zhengzhou Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China Zhongguancun Lab Beijing Peoples R China Tsinghua Univ Sch Humanities Beijing Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Prompt-based Large language Models(LLMs) are surprisingly powerful in generating natural language reasoning steps or Chains of Thoughts(CoT) for multi-hop question answering(QA). However, LLMs struggle when they lack access to necessary knowledge or when the knowledge within their parameters is outdated. Additionally, LLMs that rely solely on CoT tend to generate hallucinations during the reasoning process. To address these dilemmas, we propose the Chain of Question (CoQ) framework, a novel multi-hop QA approach. This approach decomposes a complex original question into multiple sub-questions according to a CoT to retrieve knowledge from an external knowledge base. It then answers the question process based on the retrieved knowledge in accordance with a CoT. We design that each point of thought generated during the reasoning process be supported by the knowledge retrieved in the external knowledge base. Experiments show that CoQ is effective in reducing model hallucinations, leading to higher factual accuracy than *** average, it reduces factual errors by 31% over CoT, and even by 38% on the two most commonly used models today.

关键词： Large language Models Question Answering CoT knowledge Bases

来源：评论

学校读者我要写书评

暂无评论

A Survey on Extracting knowledge Graphs by Employing natural language processing 10

A Survey on Extracting Knowledge Graphs by Employing Natural...

引用

10th international conference on Web Research, ICWR 2024

作者： Damirchi, Reza Saboohi, Hadi Amini, Amineh Islamic Azad University Karaj Branch Department of Computer Engineering Karaj Iran

ISBN: (纸本)9798350394986

We live in a world of information, and with the ever-increasing rate of content growth, we have no choice but to use machine-based solutions to manage, classify, and use it. However, the produced content is often unstructured, and for computer systems to be able to read and process it, there is no choice but to convert it to structured text. knowledge graph is one of the ways to represent data in a structured way, and its roots are found in the semantic web. There are different approaches to creating a knowledge graph, but given the significant advances in artificial intelligence, the use of natural language processing is one of the most accurate and automatic ways to do this. In this paper, an attempt has been made to review the latest methods proposed for creating a knowledge graph based on natural language processing. This first allows us to have a better understanding of this research area;in addition, the approaches used by researchers in this field are reviewed and the pros and cons of these approaches are identified. Also, the existing research areas for further work and even the initial ideas for new creative solutions are provided for researchers. © 2024 ieee.

关键词： Semantic Web

来源：评论

学校读者我要写书评

暂无评论

KrNER : A Novel Named Entity Recognition Method Based on knowledge Enhancement and Remote Supervision 22

KrNER : A Novel Named Entity Recognition Method Based on Kno...

引用

ieee 22nd international conference on Trust, Security and Privacy in Computing and Communications (TrustCom) / BigDataSE conference / CSE conference / EUC conference / ISCI conference

作者： Du, Jinhua Yin, Hao Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Beijing Peoples R China

ISBN: (纸本)9798350381993;9798350382006

Difficulty in recognizing complex entities and scarcity of annotated corpus have become the two major difficulties constraining effective information extraction from electronic medical records. Recent research focuses on utilizing complex feature engineering or large-scale pre-trained models to indirectly reduce the degradation of entity recognition caused by these two difficulties. In an attempt to directly address the difficulties, we propose KrNER, a new healthcare-oriented approach to named entity recognition. It utilizes knowledge enhancement and remote supervision techniques to effectively deal with the problem of recognizing complex entities, while guaranteeing better recognition results even in corpus-scarce contexts. Experiments show that KrNER is very effective and significantly outperforms the state-of-the-art Chinese medical named entity recognition models.

关键词： chinese electronic medical record named entity recognition deep learning pre-trained models natural language processing

来源：评论

学校读者我要写书评

暂无评论

Improving Contextual knowledge in natural language processing: An Analysis of Complex language Models and Their Uses 10

Improving Contextual Knowledge in Natural Language Processin...

引用

10th ieee Uttar Pradesh Section international conference on Electrical, Electronics and Computer engineering, UPCON 2023

作者： Rawal, Ketankumar V Vinay, A. Krishna, M Hari Singh, Navdeep Almusawi, Muntather Alzobidy, Soleman Awad Information Technology Citibank Jersey cityNJ United States School of Life Sciences Jss Academy of Higher Education & Research Division of Languages Karnataka Mysuru India Institute of Aeronautical Engineering Hyderabad Dundigal India Lovely Professional University Phagwara India The Islamic University Najaf Iraq College of Sciences and Theoretical Studies Saudi Electronic University Department of English Language and Translation Studies Saudi Arabia

ISBN: (纸本)9798350382471

Recent years have seen tremendous progress in natural language processing (NLP), mostly because of the creation of sophisticated language models. This study examines how NLP methods have developed with an emphasis on improving contextual knowledge. This work investigates the function of complex language models in enhancing contextual comprehension in NLP tasks by thoroughly analyzing their designs, training methods, and applications. Through an assessment of different cutting-edge models and their practical uses, this study offers insights into the difficulties encountered, the progress achieved, and the potential for future development in the field of contextual knowledge enhancement in natural language processing. © 2023 ieee.

关键词： Complex language Contextual knowledge natural language processing

来源：评论

学校读者我要写书评

暂无评论

Emotional knowledge Self-Distillation in Dialogue

Emotional Knowledge Self-Distillation in Dialogue

引用

2025 ieee international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Jian, Zhongquan Wang, Yancheng Wu, Weichao Yao, Junfeng Wang, Meihong Wu, Qingqiang School of Informatics Xiamen University Xiamen China School of Film Xiamen University Xiamen China School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia School of Mechanics and Civil Engineering China University of Mining & Technology Beijing China Xiamen Key Laboratory of Intelligent Storage and Computing School of Informatics Xiamen University Xiamen China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen China

ISBN: (纸本)9798350368741

Recognizing emotions in dialogues is vital for effective human-computer interaction, yet remains a challenging task in natural language processing (NLP). Previous studies in Emotion Recognition in Conversation (ERC) have primarily focused on contextual features, while overlooking the importance of emotional features in emotion recognition. To address this gap, we focus on the role of emotional features in ERC and propose a novel method, Emotional knowledge Self-Distillation (EmoKSD1), to enhance the model's emotional sensitivity. In EmoKSD, utterances are enriched with implicit 〈mask〉 tokens to represent conveyed emotions, allowing the distillation of emotional knowledge from explicit emotional tokens to implicit 〈mask〉 tokens, thereby enhancing the model's ability to perceive subtle emotions within the dialogue. Through thorough evaluations on two public ERC datasets (i.e., IEMOCAP and MELD) using proposed coarse-grained utterance distillation and fine-grained token distillation techniques, EmoKSD demonstrates superior performance compared to existing methods, highlighting the significance of emotional features in ERC. © 2025 ieee.

关键词： Dialogue System Emotion Recognition in Conversation knowledge Distillation natural language processing

来源：评论

学校读者我要写书评

暂无评论

Comparison of different natural language processing models to achieve semantic interoperability of heterogeneous asset administration shells 21

Comparison of different natural language processing models t...

引用

ieee 21st international conference on Industrial Informatics (INDIN)

作者： Beermann, Jo Benfer, Rebekka Both, Maximilian Mueller, Jochen Diedrich, Christian TH Koln Fac Proc Engn Energy & Mech Syst Cologne Germany Otto von Guericke Univ Inst Automat Technol Magdeburg Germany

ISBN: (纸本)9781665493130

Self-organizing systems represent the next level of building automation and make it possible to reduce the manual engineering effort of automation systems. For self-organizing systems to be able to interact interoperable, the system components must be mapped by uniform digital twins and described in a semantically interoperable manner. Semantic interoperability is implemented in the current research approach of Industrie 4.0 through homogeneous semantics. However, given the large number of different manufacturers of technical components, agreement on uniform semantics seems unlikely. This paper presents a method that extends the Industrie 4.0 approach to heterogeneous semantics. Semantic interoperability is realized through the automated mapping of heterogeneous vocabularies to target semantics. Models from the artificial intelligence sub-field natural language processing are used for automated mapping. In this paper, existing models of natural language processing are compared with each other in terms of their mapping accuracy. A dataset based on the ECLASS standard is being developed as a basis for the comparison. This dataset is also being used to create new models that are fine-tuned to the target vocabulary. The results show that the mapping accuracy of existing approaches improves through fine-tuning by an average of 7.5% up to 93%. In addition to the improvement through fine-tuning, this work analyses the influence of the model size on the mapping accuracy by using large language models. Moreover, it examines the integration of structured knowledge in the form of knowledge graphs.

关键词： semantic interoperability natural language processing Industrie 4.0

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 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：