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

4,244 篇 会议
102 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

2,559 篇 工学
- 2,414 篇 计算机科学与技术...
- 1,799 篇 软件工程
- 346 篇 信息与通信工程
- 274 篇 控制科学与工程
- 258 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
- 60 篇 电子科学与技术（可...
- 49 篇 仪器科学与技术
- 36 篇 光学工程
- 32 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
791 篇 管理学
- 542 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
- 303 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
73 篇 教育学
- 72 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
146 篇 artificial intel...
144 篇 deep learning
141 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

4,215 篇 英文
163 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561-570 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 Fuzzy Logic and NLP Approach to Emotion Driven Response Generation for Voice Interaction

A Fuzzy Logic and NLP Approach to Emotion Driven Response Ge...

引用

2023 IEEE international conference on Research Methodologies in knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Gopika Sri, M. Jayakarthika, K. Lavanyagayathri, V. Karthiga, G. Ilakkiya, N. Uma Mageswari, S.D. Computer Science and Business Systems R.M.K. Engineering College An Autonomous Institution Thiruvallur India R.M.K. Engineering College An Autonomous Institution Thiruvallur India

ISBN: (纸本)9798350305708

The voice modality has become the most prominent channel in the rapidly emerging field of human-computer interactions. Emotional connection is the foundation of successful communication. Our words are enhanced in human-to-human interactions by emotional undertones, vocal specifics, and contextual interpretations. When correctly interpreted, these hidden signals open the door to empathy, understanding, and effective communication. However, this emotional depth is not addressed in human computer verbal interactions. The challenge is comparable to having a conversation in which the words are understood but the feelings are not. Our idea focuses on bridging this emotional divide. We introduce an innovative approach to human-computer vocal interactions using a blend of advanced natural language processing and Fuzzy Logic-based Voice Sentiment Analysis. Because human emotions are naturally complex, we use fuzzy logic to extract nuanced sentiment scores from voice inputs. Both the content, which is obtained from NLP, and the tone, which is modified to either mirror or contradict the detected sentiment, are informed by these evaluations in the form of voice. This bonds people with AI not only through words but also through emotions. Such a system has broad applications, from emotionally intelligent virtual assistants to support lines and teletherapy solutions. By ensuring AI can not only understand but also respond aptly to human emotion, bringing AI one step closer to reality. © 2023 IEEE.

关键词： Fuzzy Logic Human-computer verbal interaction NLP Voice Sentimental Analysis

来源：评论

学校读者我要写书评

暂无评论

Sentiment Analysis: a Cognitive Perspective 8

Sentiment Analysis: a Cognitive Perspective

引用

8th international conference on Advanced Computing and Communication Systems, ICACCS 2022

作者： Panikar, Remya Bhavsar, Ramchandra Pawar, B.V. MET's Institute of Engineering Bhujbal Knowledge City Maharashtra Nashik India Kbc North Maharashtra University School of Computer Sciences Maharashtra Jalgaon India

ISBN: (纸本)9781665408165

With the phenomenal advancement of Intelligent systems, understanding natural languages and processing natural language has become a crucial task. This has found its application in various domains like conversational AI (Artificial Intelligence), social media analysis, text analytics, etc. Sentiment Analysis emerged as an important natural language processing (NLP) which aims to classify a natural language text based on its intent polarity. natural languages are comprised of varied linguistic expressions. Identifying, representing, and reasoning these expressions are necessary for a better interpretation of the sentiment conveyed in the text. In this concept paper, we put forth a detailed taxonomy of Sentiment Analysis, which categorizes possible sentiment expressions that exist in a natural language. It identifies the various sentiment components that are vital in extracting the idea, feeling, opinion, etc. from the text. The paper suggests the use of these classifications in various domains, where sentiment analysis is applied. We also put forth the proposed augmentation in the existing NLP pipeline activities to efficiently perform granular sentiment analysis. The concepts and discussions are presented with reference to the Hindi language, owing to its popularity in Indian Subcontinent. The sentiment taxonomy will be a stepping stone for developing in-depth fine-grained Sentiment Analysis. © 2022 IEEE.

关键词： Taxonomies

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence in Standard knowledge Services: Roles and Implementation

Artificial Intelligence in Standard Knowledge Services: Role...

引用

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

作者： Xiaoxin Gao Ming Sun Dong Zhang Yao Lu Yuangeng Zhu Yazhou Mu Beijing China-Power Information Technology Co. Ltd. Beijing China

ISBN: (数字)9798331528911

ISBN: (纸本)9798331528928

To improve the efficiency and accuracy of standard knowledge services, this paper explores the application of multimodal knowledge representation, knowledge graph construction, and intelligent question-and-answer (QA) systems. By leveraging deep learning and natural language processing (NLP) technologies, the system achieves automated classification, semantic analysis, and decision support for standard documents. The analysis suggests that the combination of knowledge graphs and intelligent QA systems can significantly enhance the precision of information retrieval and the automation of standardized processes. The results show that the system performs excellently in terms of response speed, user satisfaction, and performance metrics.

关键词： Deep learning Measurement Accuracy Semantics knowledge graphs Information retrieval natural language processing Stability analysis knowledge management Standards

来源：评论

学校读者我要写书评

暂无评论

Conceptualization of natural language-Based Product Configurators for New Product Development in Apparel 1

Conceptualization of Natural Language-Based Product Configur...

引用

1st international conference on Digitalization and Management Innovation, DMI 2022

作者： Uduwela, Wasana De Silva, R.K.J. Rupasinghe, Thashika Ranathunga, Lochandaka Department of Computer Science The Open University Sri Lanka Department of Textile and Apparel Engineering University of Moratuwa Sri Lanka Limited Battaramulla Sri Lanka Department of Information Technology University of Moratuwa Sri Lanka

ISBN: (纸本)9781643683782

Digital technologies enable apparel manufacturers to unlock solutions for new product development (NPD) challenges faced during the Covid-19 pandemic. A Product Configurator (PC) is the key to Mass Customization (MC) as it can employ to elicit customer requirements, which is essential in successful NPD. Even though some online PCs exist in apparel retail and product development, no one could gather customer requirements and deploy them in the NPD. This paper conceptualizes a natural language processing (NLP)-based PC on eliciting customer requirements based on a systematic literature review and a basic concept of Design Space Analysis. Finally, it is contended that this knowledge can apply in relevant software development to modernize apparel design, development and online retail. © 2023 The authors and IOS Press.

关键词： Product development

来源：评论

学校读者我要写书评

暂无评论

Enhancing Radiology Report Interpretation with Large-Scale language Models: A Two-Stage Fine-Tuning Approach 27

Enhancing Radiology Report Interpretation with Large-Scale L...

引用

27th international conference on Advanced Communications Technology, ICACT 2025

作者： Pi, Sun-Woo Park, Myeong-Soo Lee, Byoung-Dai Department of Computer Science Graduate School Kyonggi University Suwon Korea Republic of Division of AI and Computer Engineering Kyonggi University Suwon Korea Republic of

ISBN: (纸本)9791188428137

Radiology reports contain complex medical terminology and specialized knowledge, making them difficult for both patients and medical professionals to interpret. This study aims to address this challenge by developing a large-scale language model specifically designed for interpreting chest radiology reports. We focus on four key natural language processing (NLP) tasks—summarization, paraphrasing, abbreviation interpretation, and question answering—using a synthetic dataset derived from the MIMIC-CXR reports and GPT-3.5 Turbo. To enhance the model’s performance, we propose a two-stage supervised fine-tuning (SFT) process, incorporating real-world medical data from PubMedQA and MedQA, in addition to the synthetic dataset. The resulting models, Model-1 and Model-2, were evaluated based on accuracy, conciseness, and clarity, using test data not seen during training. Experimental results demonstrated that the proposed two-stage SFT method achieved strong performance across all four tasks, providing comparable performance to models such as GPT-3.5, Bard, Llama2, and MedAlpaca in key evaluation metrics, despite using a relatively smaller number of parameters. These findings suggest that synthetic data, when combined with domain-specific datasets, can significantly improve the interpretive capabilities of large-scale language models in the medical domain. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Radiology

来源：评论

学校读者我要写书评

暂无评论

GraphLingo: Domain knowledge Exploration by Synchronizing knowledge Graphs and Large language Models

GraphLingo: Domain Knowledge Exploration by Synchronizing Kn...

引用

international conference on Data engineering

作者： Duy Le Kris Zhao Mengying Wang Yinghui Wu Case Western Reserve Univ.

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

knowledge graphs (KGs) are routinely curated to provide factual data for various domain-specific analyses. Nevertheless, it remains nontrivial to explore domain knowledge with standard query languages. We demonstrate GraphLingo, a natural language (NL)-based knowledge exploration system designed for exploring domain-specific knowledge graphs. It differs from conventional knowledge graph search tools in that it enables an interactive exploratory NL query over domain-specific knowledge graphs. GraphLingo seamlessly integrates graph query processing and large language models with a graph pattern-based prompt generation approach to guide users in exploring relevant factual knowledge. It streamlines NL-based question & answer, graph query optimization & refining, and automatic prompt generation. A unique feature of GraphLingo is its capability to enable users to explore by seamlessly switching between a more ‘open’ approach and a more relevant yet ‘conservative’ one, facilitated by diversified query suggestions. We show cases of GraphLingo in curriculum suggestion, and materials scientific data search.

关键词： knowledge engineering Query processing Large language models Refining natural languages knowledge graphs Switches

来源：评论

学校读者我要写书评

暂无评论

A Light Bug Triage Framework for Applying Large Pre-trained language Model 7

A Light Bug Triage Framework for Applying Large Pre-trained ...

引用

37th IEEE/ACM international conference on Automated Software engineering (ASE)

作者： Lee, Jaehyung Han, Kisun Yu, Hwanjo Pohang Univ Sci & Technol POSTECH Pohang Gyeongsangbuk D South Korea Samsung Res Seoul South Korea

ISBN: (纸本)9781450394758

Assigning appropriate developers to the bugs is one of the main challenges in bug triage. Demands for automatic bug triage are increasing in the industry, as manual bug triage is labor-intensive and time-consuming in large projects. The key to the bug triage task is extracting semantic information from a bug report. In recent years, large Pre-trained language Models (PLMs) including BERT [4] have achieved dramatic progress in the natural language processing (NLP) domain. However, applying large PLMs to the bug triage task for extracting semantic information has several challenges. In this paper, we address the challenges and propose a novel framework for bug triage named LBT-P, standing for Light Bug Triage framework with a Pre-trained language model. It compresses a large PLM into small and fast models using knowledge distillation techniques and also prevents catastrophic forgetting of PLM by introducing knowledge preservation fine-tuning. We also develop a new loss function exploiting representations of earlier layers as well as deeper layers in order to handle the overthinking problem. We demonstrate our proposed framework on the real-world private dataset and three public real-world datasets [11]: Google Chromium, Mozilla Core, and Mozilla Firefox. The result of the experiments shows the superiority of LBT-P.

关键词： Bug triage Pre-trained language model BERT knowledge distillation Catastrophic forgetting Overthinking

来源：评论

学校读者我要写书评

暂无评论

User Story Automation in Software engineering: Insights from the Literature 12

User Story Automation in Software Engineering: Insights from...

引用

12th international conference in Software engineering Research and Innovation, CONISOFT 2024

作者： Dominguez, Francisco Antonio Mejia Quintana, Manuel A. Cinco, Ramon Rene Palacio Borrego, Gilberto Gonzalez-Lopez, Samuel Instituto Tecnológico de Sonora Ciudad Obregón Mexico Instituto Tecnológico de Sonora Navojoa Mexico Universidad Tecnológica de Nogales Departamento de Tecnologías de la Información Nogales Mexico

ISBN: (纸本)9798331532116

User stories are widely regarded as an effective method for articulating software requirements, especially within agile development. However, the brevity often encouraged in user stories can lead to a lack of detail, resulting in miscommunication and inefficiencies during development. A significant challenge is the manual extraction of user stories from unstructured text, which is labor-intensive and prone to errors. Automated extraction using natural language processing (NLP) and Machine Learning offers a promising solution. These technologies can identify and extract user roles, actions, and benefits from unstructured text, reducing the reliance on manual intervention and ensuring consistent quality. We conducted a literature review spanning the past 20 years, from 2004 to 2024, on which we identified 77 relevant articles, with only 10 focusing on the extraction of user stories using NLP techniques. These studies emphasize the need for automation in user story extraction to improve the efficiency and accuracy of requirements gathering. The findings highlight a gap in current research regarding automated extraction methods and the importance of addressing this through advanced NLP techniques. Implementing such solutions can transform the handling of user stories, making the requirements elicitation process more efficient and less error-prone, ultimately contributing to the success of agile software development projects. © 2024 IEEE.

关键词： Software design

来源：评论

学校读者我要写书评

暂无评论

An Efficient Training Framework for Chinese Text Classification: Integrating Large language Models and Reinforcement Learning

An Efficient Training Framework for Chinese Text Classificat...

引用

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

作者： Teng Wang Zhenyu Xue College of Information Engineering China Jiliang University Hangzhou China

ISBN: (数字)9798331528911

ISBN: (纸本)9798331528928

This study proposes an efficient model training framework aimed at improving the performance of hierarchical classification of Chinese text in low-computation environments. By leveraging the generative capabilities of large language models for data annotation and augmentation, a high-quality dataset is constructed. knowledge distillation techniques are applied to transfer knowledge from a complex teacher model to a lightweight student model, effectively reducing computational resources. Additionally, a reinforcement learning strategy is introduced to further optimize the student model, enabling it to surpass the teacher model in both accuracy and inference efficiency, providing a novel solution for natural language processing and other resource-constrained tasks.

关键词： Training Accuracy Annotations Computational modeling Large language models Text categorization Reinforcement learning Learning (artificial intelligence) Data models natural language processing

来源：评论

学校读者我要写书评

暂无评论

Exploring Sensory knowledge and Pre-training language Models for Chinese Metaphor Detection

Exploring Sensory Knowledge and Pre-training Language Models...

引用

international conference on Asian language processing (IALP)

作者： Qingqing Zhao Xue Xiang Zhongqing Wang Institute of Linguistics Chinese Academy of Social Sciences Beijing China School of Computer Science and Technology Soochow University Suzhou China

ISBN: (数字)9798331540852

ISBN: (纸本)9798331540869

processing of metaphorical languages has been one of the most challenging tasks in natural language processing (NLP). Recent work utilizing neural models has received notable results on metaphor detection. However, the characteristic of metaphorical languages showing an intimate relationship with sensory experiences has not been given enough attention in NLP. This study proposes an innovative model for the task of Chinese metaphor detection by incorporating the conceptual knowledge of sensory experiences into a neural network model. Experiments show that our model outperforms the state-of-the-art baseline models significantly, hence contributing to the ongoing efforts to incorporate neuro-cognitive data in NLP tasks. In addition, the effectiveness of our model helps to deepen our understanding of metaphor that sensory experiences form the crucial part of the embodied nature of metaphorical languages.

关键词： knowledge engineering Neural networks Linguistics Syntactics Feature extraction Data augmentation natural language processing

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 << < 53 54 55 56 57 58 59 60 61 62 > >>

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：