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

4,243 篇 会议
103 册 图书
50 篇 期刊文献

馆藏范围

4,396 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,558 篇 工学
- 2,413 篇 计算机科学与技术...
- 1,799 篇 软件工程
- 347 篇 信息与通信工程
- 274 篇 控制科学与工程
- 257 篇 电气工程
- 173 篇 生物工程
- 93 篇 化学工程与技术
- 78 篇 生物医学工程（可授...
- 72 篇 机械工程
- 66 篇 网络空间安全
- 59 篇 电子科学与技术（可...
- 49 篇 仪器科学与技术
- 36 篇 光学工程
- 31 篇 材料科学与工程（可...
- 30 篇 动力工程及工程热...
- 29 篇 安全科学与工程
792 篇 管理学
- 543 篇 图书情报与档案管...
- 300 篇 管理科学与工程(可...
- 104 篇 工商管理
675 篇 理学
- 302 篇 数学
- 178 篇 生物学
- 156 篇 物理学
- 118 篇 统计学（可授理学、...
- 100 篇 化学
- 68 篇 系统科学
91 篇 医学
- 72 篇 临床医学
- 64 篇 基础医学(可授医学...
- 48 篇 公共卫生与预防医...
75 篇 法学
- 68 篇 社会学
74 篇 教育学
- 73 篇 教育学
35 篇 文学
28 篇 农学
25 篇 经济学
4 篇 艺术学
2 篇 军事学

主题

1,052 篇 natural language...
318 篇 semantics
314 篇 natural language...
299 篇 natural language...
231 篇 knowledge engine...
226 篇 data mining
210 篇 machine learning
168 篇 information retr...
150 篇 training
148 篇 artificial intel...
144 篇 deep learning
140 篇 accuracy
133 篇 computational mo...
120 篇 knowledge based ...
119 篇 sentiment analys...
118 篇 ontologies
111 篇 computer science
110 篇 databases
109 篇 feature extracti...
108 篇 knowledge graph

机构

27 篇 faculty of engin...
26 篇 maharishi school...
22 篇 school of inform...
15 篇 centre of interd...
14 篇 lovely professio...
14 篇 institute of inf...
13 篇 school of comput...
13 篇 department of co...
13 篇 school of cyber ...
12 篇 beijing universi...
12 篇 school of comput...
11 篇 school of comput...
10 篇 national laborat...
10 篇 knowledge engine...
10 篇 institute of com...
9 篇 school of comput...
9 篇 beijing univ pos...
9 篇 university of ch...
9 篇 school of electr...
9 篇 moe-ms key labor...

作者

67 篇 fuji ren
37 篇 ren fuji
16 篇 bo xu
14 篇 shingo kuroiwa
14 篇 yixin zhong
14 篇 xiaojie wang
11 篇 tiejun zhao
10 篇 dongfeng cai
10 篇 lei li
10 篇 f. ren
10 篇 jingbo zhu
9 篇 s. kuroiwa
8 篇 wenju liu
8 篇 tianrui li
8 篇 zhu jingbo
8 篇 degen huang
7 篇 tanioka tetsuya
7 篇 tingting he
7 篇 guiping zhang
7 篇 vogelsang andrea...

语言

4,216 篇 英文
162 篇 其他
25 篇 中文

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

共 4396 条记录，以下是771-780 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：

Comparison of Common Crawl News & GDELT

Comparison of Common Crawl News & GDELT

引用

Annual IEEE Systems conference

作者： Ameir El Ouadi David Beskow Department of Systems Engineering United States Military Academy West Point NY USA

ISBN: (数字)9798350358803

ISBN: (纸本)9798350358810

The corpus of worldwide news is important for natural language processing, knowledge graphs, large language models, and other technical efforts. Additionally, this corpus is important for understanding the people, places, organizations, and events that interact in real-time every day. This paper compares two news datasets used for these tasks today, namely the Global Database of Events, language, and Tone (GDELT) and Common Crawl News. Our research highlights the strengths and limitations of each dataset, analyzing their content and coverage. Notably, while GDELT relies on broadcasts, prints, and web news from across the globe, Common Crawl focuses on news sites from around the world gathered through web crawling. Our analysis revealed considerable differences in where the two datasets gather their news sources.

关键词： Databases Organizations knowledge graphs Real-time systems natural language processing Task analysis

来源：评论

学校读者我要写书评

暂无评论

Coreference Resolution for Cybersecurity Entity: Towards Explicit, Comprehensive Cybersecurity knowledge Graph with Low Redundancy 1

引用

18th EAI international conference on Security and Privacy in Communication Networks, SecureComm 2022

作者： Liu, Zhengyu Su, Haochen Wang, Nannan Huang, Cheng School of Cyber Science and Engineering Sichuan University Chengdu China Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation Hefei China

ISBN: (数字)9783031255380

ISBN: (纸本)9783031255373

Cybersecurity knowledge Graph (CKG) has become an important structure to address the current cybersecurity crises and challenges, due to its powerful ability to model, mine, and leverage massive security intelligence data. To construct a comprehensive and explicit CKG with low redundancy, coreference resolution (CR) plays a crucial role as the core step in knowledge fusion. Although the research on coreference resolution techniques in natural language processing (NLP) field has made notable achievements, there is still a great gap in the cybersecurity field. Therefore, the paper first investigates the effectiveness of the existing CR models on cybersecurity corpus and presents CyberCoref, an end-to-end coreference resolution model for cybersecurity entities. We propose an entity type prediction network that not only helps to improve mention representations and provide type consistency checks, but also enables the model to distinguish the coreference among different entity types and thus run the coreference resolution more granular. To overcome the problem of implicit contextual modeling adopted by the existing CR models, we innovative propose an explicit contextual modeling method for the coreference resolution task based on semantic text matching. Finally, we improve the span representation by introducing lexical and syntactic features. The experimental results demonstrate that CyberCoref improves the F1 values on the cybersecurity corpus by 6.9% compared to existing CR models. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Cybersecurity

来源：评论

学校读者我要写书评

暂无评论

Hybrid Optimization Based BERT Model for Drug Detection in NLP 3

Hybrid Optimization Based BERT Model for Drug Detection in N...

引用

3rd IEEE Mysore Sub Section international conference, MysuruCon 2023

作者： Kruthika, C.G. Namitha Devi, N.N. Madlur, Vijayananda V Asha, K. Aruna, T.M. Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Vidya Vikas Institute of Engineering and Technology Department of Computer Science and Engineering Mysuru India Mangalore Institute of Technology and Engineering Department of Computer Science and Engineering Moodabidri India Reva University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350340358

When it comes to natural language processing (NLP), sentence embedding is a hot topic because of the knowledge it transfers to subsequent tasks. Recent studies have shown that sequential neural networks, which can be trained on large quantities of data, can be used to encode info through a one-way analysis of text sequences, making them a powerful tool for drug detection. The purpose of this study is to smear cutting-edge deep learning techniques to publicly available data in order to accurately detect and identify adverse medication reactions that have not been recorded by medical professionals (open data). This study presents a deep learning-based strategy employing Enhanced Encoder on a dataset of 10,000 reviews culled from WebMD and *** and labelled by hand (IBERT). The model is created by integrating the BERT with many parallel blocks of a single-layer deep Convolutional Neural Network (CNN) that use varying kernel sizes and filters. To deal with ambiguity, the biggest obstacle to NLP, such a combination is helpful. Cuckoo Search (CS) and Antlion Optimization (ALO) are coupled to create a hybrid optimization strategy for determining the best values for the proposed model's hyper-parameters. Results from a number of different classification methods demonstration that the suggested model meaningfully outdoes the state-of-The-Art models. © 2023 IEEE.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Constructing a Medical Domain Functional knowledge Graph with Large language Models

Constructing a Medical Domain Functional Knowledge Graph wit...

引用

IEEE international conference on Power, Intelligent Computing and Systems (ICPICS)

作者： Lintian Ji Shuxin Du Yi Qiu Hongyi Xu Xiaorui Guo Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems School of Engineering Huzhou University Huzhou China Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems School of Engineering Huzhou University Huzhou China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

The integration of medicine and artificial intelligence is creating novel opportunities to gather, analyze, and generate groundbreaking medical insights from the vast expanse of medical literature. Although these advancements hold great promise, difficulties including manual annotation and precise data retrieval, and transparency persist. Advanced natural language processing (NLP) techniques and large language models (LLMs) have shown potential in addressing these issues. This paper introduces the Medical knowledge Graph (MedKG), a comprehensive and multidisciplinary medical knowledge graph. By utilizing advanced NLP algorithms, MedKG extracts millions of entities and converts them into triples from a corpus of superior medical research papers published over the last decade. It categorizes unstructured medical information into nine distinct tags, including disease names, treatments, diagnoses, symptoms, genetics, cellular structures of diseases, patient data, medical procedures, and treatment applications, while seamlessly integrating the digital object identifier (DOI) of each paper. As the latest structured database of medical knowledge, MedKG serves as a powerful tool for accelerating medical research and lays the groundwork for constructing a more extensive medical knowledge graph using full-text medical articles. Moreover, our research establishes a foundation for practical knowledge management systems based on text mining, applicable not only to complex medical systems but also to other professional domains.

关键词： Text mining Annotations Large language models Pipelines knowledge graphs Prediction algorithms natural language processing Object recognition Medical diagnostic imaging Diseases

来源：评论

学校读者我要写书评

暂无评论

Extraction of Subjective Information from Large language Models

Extraction of Subjective Information from Large Language Mod...

引用

IEEE Annual international Computer Software and Applications conference (COMPSAC)

作者： Atsuya Kobayashi Saneyasu Yamaguchi Electrical Engineering and Electronics Kogakuin University Graduate School Tokyo Japan Department of Information and Communications Engineering Kogakuin University Tokyo Japan

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

Large language Models (LLMs) have been advancing natural language processing technologies for several years. Especially in the last year, generative AI (Artificial Intelligence) models have improved remarkably and attracted attention. Then, there has been a great discussion about these models, such as extracting the knowledge in these models. However, these existing works were mainly for the extraction of objective information. In this paper, we study the extraction of subjective information from LLMs by focusing on GPT, Gemini Pro, and Claude2. We then show that such information extraction can be severely limited in LLMs, but that information extraction is possible from some models.

关键词： Generative AI Large language models Computational modeling Focusing Information retrieval Software natural language processing

来源：评论

学校读者我要写书评

暂无评论

The Research on Current Status of Digital Technology System of Electric Power Emergency Plan

The Research on Current Status of Digital Technology System ...

引用

international conference on Electronics Information and Emergency Communication

作者： Cheng Guan Diange Fang Tao Li Xun Zhang Feng Wei Hong Zhao Department of Electrical Emergency Response Grid Intelligent Power Grid Research Institute Beijing China Department of Electricity Management Grid Corporation of China Beijing China Department of Electrical Emergency Response Grid Gansu Electric Power Scientific Research Institute Gansu China

ISBN: (数字)9798350361896

ISBN: (纸本)9798350361902

Based on the knowledge system of electric power emergency command, study the digitalization technology based on the theories of natural language processing, knowledge mapping, scenario-coping, etc., realize the autonomous recognition, categorization, disassembly and optimization of the text of the emergency plan, establish the ontology model of the plan by using the knowledge mapping, study the scenario-coping technology, set up the relational network model, and establish the digitalization technology system of electric power emergency plan by combining the above theoretical studies. The above theories are combined to establish a digital technology system for electric power emergency plans.

关键词： knowledge engineering Industries Text recognition knowledge based systems Ontologies natural language processing Power grids

来源：评论

学校读者我要写书评

暂无评论

Enhancing Agricultural Advisory Services with Multilingual LLaMA and RAG

Enhancing Agricultural Advisory Services with Multilingual L...

引用

Next Generation Communication & Information processing (INCIP), international conference on

作者： G. Bharathi Mohan C.M. Jayanth Adhitya A. Mithilesh Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

Agriculture, being a vital component of global food security, necessitates the development of innovative solutions to address the knowledge gap experienced by numerous farmers, particularly in developing countries where access to expert advice is scarce. In these areas, agricultural producers often depend on helplines for essential guidance, but the exorbitant costs and limited accessibility of these services create substantial obstacles. By automating responses to agricultural inquiries, the burden on traditional helpline systems can be alleviated, enabling farmers to access prompt and precise information. The combination of artificial intelligence and agriculture offers a chance to tackle these challenges, with advanced language models, especially transformers, demonstrating significant potential in comprehending intricate agricultural queries and delivering appropriate responses. This paper investigates how large language models (llms) can simplify the process of finding answers for farmers by utilizing their advanced language processing abilities. By analyzing a vast collection of over four million farmer inquiries from tamil nadu, india, encompassing diverse agricultural scenarios, this study showcases the efficacy of llms in bridging information gaps and equipping farmers with immediate access to crucial knowledge.

关键词： Precision agriculture Large language models Biological system modeling Food security Information processing Metadata Transformers Solids natural language processing Multilingual

来源：评论

学校读者我要写书评

暂无评论

CoRAL: a Context-aware Croatian Abusive language Dataset 2

CoRAL: a Context-aware Croatian Abusive Language Dataset

引用

2nd conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th international Joint conference on natural language processing, AACL-IJCNLP 2022

作者： Shekhar, Ravi Karan, Mladen Purver, Matthew Cognitive Science Research Group School of Electronic Engineering & Computer Science Queen Mary University of London United Kingdom Department of Knowledge Technologies Jo ef Stefan Institute Ljubljana Slovenia

ISBN: (纸本)9781959429043

In light of unprecedented increases in the popularity of the internet and social media, comment moderation has never been a more relevant task. Semi-automated comment moderation systems greatly aid human moderatorsby either automatically classifying the examples or allowing the moderators to prioritizewhich comments to consider first. However,the concept of inappropriate content is oftensubjective, and such content can be conveyedin many subtle and indirect ways. In this work,we propose CoRAL1 a language and culturally aware Croatian Abusive dataset covering phenomena of implicitness and relianceon local and global context. We show experimentally that current models degrade whencomments are not explicit and further degradewhen language skill and context knowledgeare required to interpret the comment. © AACL-IJCNLP *** rights reserved

关键词：

来源：评论

学校读者我要写书评

暂无评论

Word Sense Disambiguation for Hindi language Using Neural Network 1st

Word Sense Disambiguation for Hindi Language Using Neural Ne...

引用

1st international conference on Advancements in Smart Computing and Information Security, ASCIS 2022

作者： Mishra, Binod Kumar Jain, Suresh Computer Science and Engineering Department Medi-Caps University Indore India

ISBN: (纸本)9783031230912

One of the major hurdles in the development of natural language processing applications is ambiguity and the process to solve is known as Word Sense Disambiguation. It is useful to determine the appropriate meaning of polysemy words in each context using computational methods. In every language, ambiguity exists, and to resolve this knowledge, Supervised, and Unsupervised based approaches are used. Over the past ten years, the Indian government has launched a large number of digital services for its citizens. To better serve Indian citizens, these services are primarily based on Hindi or other Indian languages. For it to be easily accessed by web portals or any electronic device, natural language processing is required. Since English and other languages such as Chinese, Japanese, and Korean have abundant resources for natural language processing application development. Due to the limited resources available for disambiguating polysemous words in Hindi and other Indian languages, building applications based on these languages becomes challenging. This paper provides a very gentle introduction to the simplest version of neural network techniques for solving ambiguity problems who are not familiar with integration with IndoWordNet. The outcomes demonstrate that the suggested technique performs consistently and significantly better than the alternatives. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Portals

来源：评论

学校读者我要写书评

暂无评论

A Review of Methods Using Large language Models in News Recommendation Systems

A Review of Methods Using Large Language Models in News Reco...

引用

ACIS international conference on Software engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD)

作者： Xinmiao Li Shuang Feng Xin Zhang School of Computer and Cyber Sciences Communication University of China Beijing China

ISBN: (数字)9798350391954

ISBN: (纸本)9798350391961

Large language Models (LLMs) are widely used in natural language processing tasks due to their powerful semantic understanding and knowledge integration capabilities. Numerous existing recommendation studies consider recommendation tasks as a type of natural language processing, and thus LLMs have consequently brought new changes to the recommendation system paradigm. Existing research on recommendations using LLMs partly utilizes their rich data information, fine-grained user profiling, and expanded recommendation content to improve recommendation effectiveness. Additionally, some and partly studies directly uses LLMs to implement a generative recommendation paradigm. This paper adopts the literature review method to systematically sort out the current research status of news recommendation based on LLMs and classifies and summarizes the relevant research. To comprehensively understand the research in the field of news recommendation using LLMs, this paper introduces the current major work in the field of news recommendation from the two categories of generative LLM-assisted recommendation and direct generative recommendation and summarizes the current work as well as the potential future research directions and challenges.

关键词： Technological innovation Accuracy Reviews Large language models Semantics natural language processing User experience

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440页 << < 74 75 76 77 78 79 80 81 82 83 > >>

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：