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681-690 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：

CSL: A Large-scale Chinese Scientific Literature Dataset 29

CSL: A Large-scale Chinese Scientific Literature Dataset

引用

29th international conference on Computational Linguistics, COLING 2022

作者： Li, Yudong Zhang, Yuqing Zhao, Zhe Shen, Linlin Liu, Weijie Mao, Weiquan Zhang, Hui School of Information Engineering China Shenzhen University School of Computer Science and Software Engineering China Tencent AI Lab United States Information Technology Center for National Science and Technology Infrastructure Beijing China

Scientific literature serves as a high-quality corpus, supporting a lot of natural language processing (NLP) research. However, existing datasets are centered around the English language, which restricts the development of Chinese scientific NLP. In this work, we present CSL, a large-scale Chinese Scientific Literature dataset, which contains the titles, abstracts, keywords and academic fields of 396k papers. To our knowledge, CSL is the first scientific document dataset in Chinese. The CSL can serve as a Chinese corpus. Also, this semi-structured data is a natural annotation that can constitute many supervised NLP tasks. Based on CSL, we present a benchmark to evaluate the performance of models across scientific domain tasks, i.e., summarization, keyword generation and text classification. We analyze the behavior of existing text-to-text models on the evaluation tasks and reveal the challenges for Chinese scientific NLP tasks, which provides a valuable reference for future research. Data and code are available at https://***/ydli-ai/CSL . © 2022 Proceedings - international conference on Computational Linguistics, COLING. All rights reserved.

关键词： natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

Research on Chinese Event Extraction Method with No-trigger 6

Research on Chinese Event Extraction Method with No-trigger

引用

6th international conference on Computer Network, Electronic and Automation, ICCNEA 2023

作者： Li, Yi Geng, Chaoyang Yang, Dan Xi'an Technological University School of Computer Science and Engineering Xi'an China

ISBN: (纸本)9798350305388

Event extraction is a key task in the field of natural language processing, which aims to structure text in preparation for tasks such as subsequent knowledge graph construction. Most of the existing event extraction methods are difficult to handle well the case of containing multiple events within the same document and to distinguish efficiently for negative obfuscation samples. In this paper, we propose a model called RCEENT (A RoBERTa-wwm-based Chinese Event Extraction method with No-Trigger). RCEENT based on RoBERTa-wwm encoder and PTPCG (Pseudo-Trigger-aware Pruned Complete Graph) decoder to address the above problems. The RCEENT model can improve the accuracy of the event detection module without adding too much complexity by using multiple binary classifiers, and improve the results of the event extraction task by using graph pruning and PTPCG model for decoding. RCEENT achieves a 1.4% F1 score improvement in the event detection task and a 3.9% F1 score improvement in the event extraction task on Duee-fin compared to the latest baseline method. © 2023 IEEE.

关键词： Decoding

来源：评论

学校读者我要写书评

暂无评论

CADE: The Missing Benchmark in Evaluating Dataset Requirements of AI-enabled Software 30

CADE: The Missing Benchmark in Evaluating Dataset Requiremen...

引用

30th IEEE international Requirements engineering conference (RE)

作者： Barzamini, Hamed Rahimi, Mona Northern Illinois Univ Dept Comp Sci De Kalb IL 60115 USA

ISBN: (数字)9781665470001

ISBN: (纸本)9781665470001

The inductive nature of artificial neural models makes dataset quality a key factor of their proper functionality. For this reason, multiple research studies proposed metrics to assess the quality of the models' datasets, such as dataset correctness, completeness, and consistency. However, these studies commonly lack a point of reference against which the proposed quality metrics could be assessed. To this end, this paper proposes a generic process that extracts the necessary knowledge to build a reliable reference point for the purpose of explanation, assessment, and augmentation of the AI-software dataset. This process automatically builds a benchmark specific to the software operational domain, interprets the training and validation datasets of AI-enabled perception software systems, and evaluates the dataset semantic quality and completeness relative to the benchmark. We implemented this process within a framework called Concept Augmentation and Dataset Evaluation (CADE), which leverages a series of novel natural language and image processing techniques to construct a semantic benchmark with respect to the domain specifications. The application of CADE to three commonly-used autonomous driving datasets showed several common weaknesses present in the arbitrarily-collected datasets against the encoded domain specifications, demonstrating dataset divergence from the domain concepts and under-represented variances of the concepts in the data. The qualitative evaluation results showed an average of about 75% relevancy of CADE generated topics.

关键词： Measurement Training Image processing Semantics natural languages Benchmark testing Software systems

来源：评论

学校读者我要写书评

暂无评论

Power grid engineering data knowledge retrieval and graph construction technology 5

Power grid engineering data knowledge retrieval and graph co...

引用

5th IEEE international Electrical and Energy conference, CIEEC 2022

作者： Zhou, Jia Li, Longfang Bai, Yang State Grid Nantong Power Supply Company Nantong China

ISBN: (数字)9781665411042

ISBN: (纸本)9781665411042

The power grid infrastructure project has the characteristics of large scale, long period, and multiple subjects. Therefore, it is determined that a large amount of data with rich sources and complex formats will be generated in the whole process of power grid engineering from design, construction to acceptance. However, due to the lack of effective data extraction and sorting technical means, these data results have a single search method and related queries are difficult, and they cannot be displayed intuitively. This article takes the noise-containing multi-source heterogeneous infrastructure engineering data as the research object, and relies on natural language processing technology to carry out research on the information extraction model and method and the knowledge map construction technology. This method automatically analyzes the natural semantics contained in the text data of the power grid infrastructure project, excavates the valuable information contained therein, and then constructs a data knowledge graph to realize hierarchical storage, visual display and related information recommendation. © 2022 IEEE.

关键词： knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Enhancing natural language processing: A Comprehensive Review of Retrieval Augmented Generation

Enhancing Natural Language Processing: A Comprehensive Revie...

引用

Sustainable Expert Systems (ICSES), international conference on

作者： Aniket K Shahade Priyanka V Deshmukh Symbiosis Institute of Technology Pune Campus Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Retrieval augmented generation (RAG) has emerged as a promising approach in natural language processing, combining retrieval and generation techniques to produce high-quality text. By incorporating external knowledge into the generation process, RAG addresses the limitations of generative models that rely solely on learned information. This review explores the foundational principles and architecture of RAG, which typically involves a two-step process: retrieving relevant information based on the input query and generating text informed by both the query and the retrieved knowledge. The review discusses key considerations for the retriever and generator components, as well as the integration of retrieved knowledge into the generative model. By understanding the underlying principles and architecture of RAG, researchers and practitioners can leverage this powerful technique to improve the quality and contextual relevance of text generation tasks.

关键词： Ethics Reviews Refining Merging Information processing Reliability engineering Question answering (information retrieval) Generators Protection Expert systems

来源：评论

学校读者我要写书评

暂无评论

Integrating the Innovations of GPT-3 and U-Net for Text-to-Image Creation in Digital Art Galleries

Integrating the Innovations of GPT-3 and U-Net for Text-to-I...

引用

2024 OPJU international Technology conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Thethi, H. Pal Rajitha, Akula Revathi, V. Chourasia, Riya Yadav, Dinesh Kumar Abed, Mohammed Najem Lovely Professional University Phagwara India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Applied Sciences Bangalore India GyanGanga Institute of Technology and Sciences DoEC M.P Jabalpur India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India National University of Science and Technology College of Dentist Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

The new method known as 'GPT3-UNet-Art,' which was developed for use in online art exhibitions, combines the finest features of GPT-3 and U-Net. The recommended approach links textual narratives with visually arresting artwork by utilizing cutting-edge deep learning and natural language processing capabilities. The three main algorithms are 'Text Understanding with GPT-3,' 'Image Generation with UNet,' and 'Artistic Enhancement.' Together, these algorithms produce a more imaginative final product, consistently improve the original graphics, and extract the text's semantic meaning. Math equations and flowcharts can aid in simplifying the procedures. The proposed technique outperforms other popular ones such as DALL· E, Stack GAN, Big GAN, VQ-VAE-2, Art producer, and Deep Dream in terms of performance. Picture actuality, text-to-image consistency, and creative variation are what set it apart. It upholds moral principles, excels at computation, and improves user relationships. This approach opens up the process of creating art to a wider audience. 'GPT3UNet-Art' represents a significant advancement in online art creation. It quickly transforms textual descriptions into breathtaking works of art, providing a venue for artists and art enthusiasts to investigate the relationship between words and visual art. Because it is highly realistic, incorporates a wide variety of artistic mediums, is user-friendly, and is ethically obvious, this approach is entirely novel in the realm of digital art presentations. © 2024 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Aspect-Based Sentiment Analysis of Bangla Comments on Entertainment Domain 25

Aspect-Based Sentiment Analysis of Bangla Comments on Entert...

引用

25th international conference on Computer and Information Technology, ICCIT 2022

作者： Sultana, Nasrin Sultana, Rehena Rasel, Risul Islam Hoque, Mohammed Moshiul Chittagong University of Engineering & Technology Department of Cse Chittagong4349 Bangladesh University of Chittagong Department of Cse Chittagong4331 Bangladesh Chittagong Independent University Department of Cse Chittagong4000 Bangladesh

ISBN: (纸本)9798350346022

Low-resource natural language processing is getting more attention nowadays. Aspect-Based Sentiment Analysis (ABSA) from a high-resource language such as English becomes unchallenging because of sufficient datasets and experimentation tools. However, Aspect-Based Sentiment Analysis from low-resource languages such as Bangla is quite hard. So, many researchers are investing their time and knowledge in low-resource natural language processing. In this paper, we are proposing a Bangla Aspect-Based Sentiment Analysis model using Bangla natural language processing. We have collected 4012 Bangla text comments related to cricket, drama, movie, and music from YouTube. We have applied some very prominent supervised machine learning techniques such as Support Vector Classifier (SVC), Random Forest (RF), and Linear Regression (LR). We have achieved more than 75% accuracy in classifying positive, negative, and neutral sentiments and 80% accuracy in extracting aspects from Bangla texts. Finally, we used publicly available datasets to test our proposed model's generalizability. Furthermore, we find that our proposed approach surpasses earlier related research. © 2022 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Research of knowledge-Enhanced Large language Model Based on knowledge Graph

Research of Knowledge-Enhanced Large Language Model Based on...

引用

Artificial Intelligence and Computer engineering (ICAICE), international conference on

作者： Qi Meng Zhenglong Wu Zhongshi Zhao Xi'nan Lian Postgraduate Brigade PLA Arm Academy of Artillery and Air Defense Hefei China Weapon Engineering Department PLA Arm Academy of Artillery and Air Defense Hefei China

ISBN: (数字)9798331528911

ISBN: (纸本)9798331528928

The rapid development and wide application of knowledge Graph(KG) and Large language Model(LLM) have demonstrated strong basic capabilities of artificial intelligence. In the last two years, related research has explored the combination of KG and LLM to improve the model's ability in text understanding and inference. In view of this kind of research, the basic knowledge of KG enhancing LLM is systematically introduced, and how KG enhances LLM from the perspective of functional realization is analyzed, and the existing research results of KG enhancing LLM is also summarized, which provides reference for further research on LLM enhanced by KG.

关键词： Training Inference mechanisms Large language models Computational modeling Semantics knowledge graphs natural language processing Inference algorithms Graph neural networks Hardware acceleration

来源：评论

学校读者我要写书评

暂无评论

Construction of a Signal-class Experimental Platform Based on Python 3

Construction of a Signal-class Experimental Platform Based o...

引用

3rd international conference on Computer Technologies, ICCTech 2024

作者： Lu, Ying Ming, Niankun Dai, Fei Wang, Mingyang Southwest Forestry University School of Big Data and Intelligent Engineering Kunming China

ISBN: (纸本)9798350395419

There are many advanced concepts and abstractions involved in signal-class courses, which require complex mathematical formulas to be deduced and proven. The derivation process of these formulas is difficult to follow, and there is a challenge in correlating them to the experimental results, making it difficult for students to understand. In response to the non-open-source nature of Matlab, Python, as a free and open-source programming language, has powerful features and rich third-party library resources, supporting various signal processing methods and algorithms, showing unique advantages and effects. Currently, existing experimental simulation platforms for signal courses are often designed for a certain type of course, such as Digital Signal processing, Communication Principle, Signals and Systems, etc. These platforms only provide limited cases and experiments, and the course content they cover is relatively single and independent, lacking the connection and integration of knowledge points between different courses. To improve this situation, this article chose PyQt5 as the development tool library, with its excellent performance and rich resources, to design and develop an experimental simulation platform for signal-class courses. This platform closely follows the teaching syllabus and divides the platform into multiple modules according to the division of teaching content. Each module contains corresponding course knowledge points and experimental simulations. The platform also has various teaching service functions such as online self-study and teaching guidance, which can help teachers make teaching more vivid and interesting, and can also help students better understand abstract theories and concepts. The platform has been put into teaching and learning in our college and other brotherly universities, which receiving positive feedback and praise from teachers and students. © 2024 IEEE.

关键词： Python

来源：评论

学校读者我要写书评

暂无评论

natural language processing using Graph Neural Network for Text Classification

Natural Language Processing using Graph Neural Network for T...

引用

2022 IEEE international conference on knowledge engineering and Communication Systems, ICKES 2022

作者： Kumar, V. Suresh Alemran, Ahmed Karras, Dimitrios A. Kant Gupta, Shashi Kumar Dixit, Chandra Haralayya, Bhadrappa Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Information Technology Chennai India Medicine College-Misan University Physics Department Amarah Iraq General Department Psachna Greece Integral University Department of Cse Lucknow India Dr. Shakuntala Misra National Rehabilitation University Department of Physics Lucknow India Lingaraj Appa Engineering College Department of Mba Bidar India

ISBN: (纸本)9781665456371

The boom of the technological area has given rise to numerous new applications that actually rule the entire world. Some of them mainly are the social media networks like the Twitter, Gmail and Facebook. Lot of text and content are received to millions of people across the work. It has become very essential to be surer whether the content is fake, or isit malicious or is it a really important content. To classify these contents, text classification techniques are being widely used across the globe. This has invented a new technical world of natural language processing where it becomes very important to process the texts and images before we actually use them. In this paper, we have introduced the use of Graph Neural Networks (GNN) to classify the text according to their content. The use of GNN is done as they work well for 2D vectors and texts are in two dimensions. Computation of Self-Organizing Maps (SOM) are done to compute the nearest neighbors in the graphs and it calculates the actual distances between the neighbors. This process is used in classifying the text and performs better when compared to the existing techniques. © 2022 IEEE.

关键词： Self organizing maps

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 << < 65 66 67 68 69 70 71 72 73 74 > >>

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：