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411-420 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：

ArabicSense: A Benchmark for Evaluating Commonsense Reasoning in Arabic with Large language Models 4

ArabicSense: A Benchmark for Evaluating Commonsense Reasonin...

引用

4th Workshop on Arabic Corpus Linguistics, WACL 2025

作者： Lamsiyah, Salima Zeinalipour, Kamyar El Amrany, Samir Brust, Matthias Maggini, Marco Bouvry, Pascal Schommer, Christoph University of Luxembourg Luxembourg University of Siena DIISM Via Roma 56 Siena53100 Italy

ISBN: (纸本)9798891762206

Recent efforts in natural language processing (NLP) commonsense reasoning research have led to the development of numerous new datasets and benchmarks. However, these resources have predominantly been limited to English, leaving a gap in evaluating commonsense reasoning in other languages. In this paper, we introduce the ArabicSense Benchmark, which is designed to thoroughly evaluate the world-knowledge commonsense reasoning abilities of large language models (LLMs) in Arabic. This benchmark includes three main tasks: first, it tests whether a system can distinguish between natural language statements that make sense and those that do not;second, it requires a system to identify the most crucial reason why a nonsensical statement fails to make sense;and third, it involves generating explanations for why statements do not make sense. We evaluate several Arabic BERT-based models and causal LLMs on these tasks. Experimental results demonstrate improvements after fine-tuning on our dataset. For instance, AraBERT v2 achieved an 87% F1 score on the second task, while Gemma and Mistral-7b achieved F1 scores of 95.5% and 94.8%, respectively. For the generation task, LLaMA-3 achieved the best performance with a BERTScore F1 of 77.3%, closely followed by Mistral-7b at 77.1%. All codes and the benchmark is publicly available. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Stance Detection for Social Text: Inference-Enhanced Multi-Task Learning with Machine-Annotated Supervision

Stance Detection for Social Text: Inference-Enhanced Multi-T...

引用

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

作者： Pi, Qiankun Lu, Jicang Sun, Yepeng Fan, Qinlong Zhou, Xukun Shang, Shouxin State Key Laboratory of Mathematical Engineering and Advanced Computing Henan Zhengzhou China School of Information Renmin University of China Beijing China

ISBN: (纸本)9798350368741

Stance detection, a natural language processing technique, captures user attitudes on controversial social media topics. However, the semantic ambiguity of social texts makes accurate stance determination challenging. Existing annotated data is often domain-specific, resulting in poor model generalization for unseen targets and cross-domain scenarios. To tackle this challenge, we introduce inference tasks related to stance detection as auxiliary tasks and use a multitask learning approach to improve the model's understanding of textual semantics. Meanwhile, to alleviate the scarcity of annotated data, we use argumentation corpus with abundant resources as the source domain to train the basic stance detection model. We integrate this model with a large-scale framework to facilitate weakly supervised learning for machine annotation of unlabeled social texts, thereby boosting the model's adaptability across different targets and domains. Experimental results on four Twitter datasets demonstrate that our method can significantly improve the model's stance discriminative ability and generalization performance. © 2025 IEEE.

关键词： argumentation corpus machine annotation multitask learning stance detection

来源：评论

学校读者我要写书评

暂无评论

Multi-phase difference enhanced fusion for tumor and pancreas segmentation

Multi-phase difference enhanced fusion for tumor and pancrea...

引用

2024 international conference on Computer Vision and Image processing, CVIP 2024

作者： He, Chongyu Zhou, Fugen Liu, Bo Li, Jiaping Zhou, Dinghua Image Processing Center School of Astronautics Beihang University Beijing100191 China Department of Interventional Oncology The First Affiliated Hospital Sun Yat-Sen University Guangdong Guangzhou China Department of Hepatobiliary Surgery PLA Rocket Force Characteristic Medical Center Beijing China

ISBN: (纸本)9781510688384

Multi-phase contrast-enhanced computed tomography (CECT) has been shown to effectively refine the segmentation accuracy for both tumors and organs. Nevertheless, enhancing the segmentation accuracy of the pancreas and pancreatic tumors using multi-phase CECT remains a challenge, which is crucial for subsequent clinical analysis and treatment. Current methods either simply concatenate multi-phase features along the channel direction or utilize attention mechanisms to perform weighted summation of features across phases, neglecting the contribution of phase-specific difference features to segmentation accuracy. In view of this, this paper proposes a multi-phase segmentation method for the pancreas and pancreatic tumors, improving segmentation accuracy by enhancing the phase-specific difference features. Additionally, we propose a training strategy to improve the segmentation accuracy of model when only single-phase CECT is available. The process achieved by distilling the learned knowledge of a segmentation model trained on multi-phase CECT. The proposed method and training strategy proved effective through experiments conducted on a privately collected multiphase CECT dataset of pancreatic tumors. © 2025 SPIE.

关键词： Computerized tomography

来源：评论

学校读者我要写书评

暂无评论

VisualRWKV: Exploring Recurrent Neural Networks for Visual language Models 31

VisualRWKV: Exploring Recurrent Neural Networks for Visual L...

引用

31st international conference on Computational Linguistics, COLING 2025

作者： Hou, Haowen Zeng, Peigen Ma, Fei Yu, Fei Richard Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shool of Information Technology Carleton University Canada

ISBN: (纸本)9798891761964

Visual language Models (VLMs) have rapidly progressed with the recent success of large language models. However, there have been few attempts to incorporate efficient linear Recurrent Neural Networks (RNNs) architectures into VLMs. In this study, we introduce VisualRWKV, the first application of a linear RNN model to multimodal learning tasks, leveraging the pre-trained RWKV language model. We propose a data-dependent recurrence and sandwich prompts to enhance our modeling capabilities, along with a 2D image scanning mechanism to enrich the processing of visual sequences. Extensive experiments demonstrate that VisualRWKV achieves competitive performance compared to Transformer-based models like LLaVA-1.5 on various benchmarks. Compared to LLaVA-1.5, VisualRWKV has a speed advantage of 3.98 times and can save 54% of GPU memory when reaching an inference length of 24K tokens. To facilitate further research and analysis, we have made the checkpoints and the associated code publicly accessible at the following GitHub repository: https://***/howard-hou/VisualRWKV. © 2025 Association for Computational Linguistics.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

A NLP Approach to Quantify Resilience in Cyber-Socio-Technical Systems with LLM Agents 6

A NLP Approach to Quantify Resilience in Cyber-Socio-Technic...

引用

6th international conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： De Nicola, Antonio Migliore, Maria Guariglia Mele, Ida Villani, Maria Luisa Via Anguillarese 301 Rome00123 Italy Sapienza University of Rome Department of Mechanical and Aerospace Engineering Via Eudossiana 18 Rome00184 Italy Antonio Ruberti National Research Council Via dei Taurini 19 RomeI-00185 Italy

Incorporating cyber artifacts into Cyber-Socio-Technical Systems (CSTSs) poses new challenges to human safety due to their increasingly unpredictable behavior. Resilience is the ability to adapt, recover, and bounce back from challenges, setbacks, or adversity. Quantifying the resilience of CSTSs requires the identification of leading or lagging indicators. Among the leading indicators, allostatic load measures the level of systemic tension accumulated from misalignments in the perspectives of system actors regarding how work should be performed. In this paper, we propose a novel approach based on natural language processing (NLP) to measure allostatic load. This approach involves lightweight modelling of process perspectives, extraction of token vectors from process function descriptions, and computing vector similarity by using the Dice similarity algorithm. Then, allostatic load is defined as the complement to one of the similarity value. An example application concerning a chemical spill in a hospital laboratory demonstrates the method's practical use. © 2024 The Authors. Published by Elsevier B.V.

关键词： Cyber-Socio-Technical System Large language Model natural language processing Resilience Safety

来源：评论

学校读者我要写书评

暂无评论

Team IndiDataMiner at IndoNLP 2025: Hindi Back Transliteration - Roman to Devanagari using LLaMa 1

Team IndiDataMiner at IndoNLP 2025: Hindi Back Transliterati...

引用

1st Workshop on natural language processing for Indo-Aryan and Dravidian languages, IndoNLP 2025

作者： Kumar, Saurabh Kakadiya, Dhruvkumar Babubhai Singh, Sanasam Ranbir Department of Computer Science and Engineering Indian Institute of Technology Guwahati India

ISBN: (纸本)9798891762145

The increasing use of Romanized typing for Indo-Aryan languages on social media poses challenges due to its lack of standardization and loss of linguistic richness. To address this, we propose a sentence-level back-transliteration approach using the LLaMa 3.1 model for Hindi. Leveraging fine-tuning with the Dakshina dataset, our approach effectively resolves ambiguities in Romanized Hindi text, offering a robust solution for converting it into the native Devanagari script. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Beyond Relevant Documents: A knowledge-Intensive Approach for Query-Focused Summarization Using Large language Models 27th

Beyond Relevant Documents: A Knowledge-Intensive Approach fo...

引用

27th international conference on Pattern Recognition, ICPR 2024

作者： Zhang, Weijia Huang, Jia-Hong Vakulenko, Svitlana Xu, Yumo Rajapakse, Thilina Kanoulas, Evangelos University of Amsterdam Amsterdam Netherlands Amazon Barcelona Spain University of Edinburgh Edinburgh United Kingdom

ISBN: (纸本)9783031784941

Query-focused summarization (QFS) is a fundamental task in natural language processing with broad applications, including search engines and report generation. However, traditional approaches assume the availability of relevant documents, which may not always hold in practical scenarios, especially in highly specialized topics. To address this limitation, we propose a novel knowledge-intensive approach that reframes QFS as a knowledge-intensive task setup. This approach comprises two main components: a retrieval module and a summarization controller. The retrieval module efficiently retrieves potentially relevant documents from a large-scale knowledge corpus based on the given textual query, eliminating the dependence on pre-existing document sets. The summarization controller seamlessly integrates a powerful large language model (LLM)-based summarizer with a carefully tailored prompt, ensuring the generated summary is comprehensive and relevant to the query. To assess the effectiveness of our approach, we create a new dataset, along with human-annotated relevance labels, to facilitate comprehensive evaluation covering both retrieval and summarization performance. Extensive experiments demonstrate the superior performance of our approach, particularly its ability to generate accurate summaries without relying on the availability of relevant documents initially. This underscores our method’s versatility and practical applicability across diverse query scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Query languages

来源：评论

学校读者我要写书评

暂无评论

Automatic Generation Method for Autonomous Driving Simulation Scenarios Based on Large language Model

Automatic Generation Method for Autonomous Driving Simulatio...

引用

international conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Zhou, Xiaoji Huang, Yuqi Zhang, Jian Shao, Jingjin Pan, Deng Li, Penghui China Automotive Engineering Research Institute Co. Ltd. Chongqing401122 China School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9789819639762

The virtual simulation testing and verification of autonomous driving systems require large-scale test scenarios. Existing methods for scenario construction are often limited by human experience in user interaction and scene generation, leading to inefficiencies. To overcome this challenge, we propose a method that leverages LLaMA 3.1, an open-source large language model, within an intelligent collaborative framework to generate simulation scenarios using natural language commands. By integrating domain knowledge, R157 regulations, OpenX scenario samples, and other textual resources, the model generates text through training, fine-tuning, and quantization, with support for Chinese. It standardizes the generation of OpenX format simulation scenario files and references R157 regulation to automatically create cut-in scenarios. The application of these generated scenarios in simulation testing for the ALKS system demonstrates the effectiveness and feasibility of this scenario generation approach. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

A Novel Generative AI-Powered Approach for Sentiment Analysis: Enhancing Deep Learning Models with Contextual Understanding 3

A Novel Generative AI-Powered Approach for Sentiment Analysi...

引用

3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Naik, Mahendra Shridhar Chaitra, S.N. Shashikala, K.S. New Horizon College of Engineering Department of Electronics & Communication Bengaluru India Gm Institute of Technology Department of Electronics & Communication Davangere India New Horizon College of Engineering Department of Artificial Intelligence & Machine Learning Bengaluru India

ISBN: (纸本)9798331515911

Sentiment analysis, a pivotal aspect of natural language processing (NLP), has seen notable advancements with the advent of deep learning models. Despite these advancements, challenges persist in accurately capturing nuanced emotions and understanding context-dependent sentiments. This paper introduces an innovative method that integrates generative adversarial networks (GANs) into deep learning architectures to enhance sentiment analysis. By leveraging GAN's ability to generate contextually relevant text, the proposed approach enriches the training data, thereby improving the model's ability to discern subtle sentiment variations. Our extensive experiments on benchmark datasets reveal that our method surpasses traditional deep learning models, achieving a 5% improvement in accuracy, a 7% higher F1-score, and demonstrating superior robustness to sarcasm and irony. © 2025 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

iText2KG: Incremental knowledge Graphs Construction Using Large language Models 25th

iText2KG: Incremental Knowledge Graphs Construction Using La...

引用

25th international conference on Web Information Systems engineering, WISE 2024

作者： Lairgi, Yassir Moncla, Ludovic Cazabet, Rémy Benabdeslem, Khalid Cléau, Pierre INSA Lyon CNRS Universite Claude Bernard Lyon 1 LIRIS UMR5205 Villeurbanne69621 France GAUC Lyon France

ISBN: (纸本)9789819605729

Most available data is unstructured, making it challenging to access valuable information. Automatically building knowledge Graphs (KGs) is crucial for structuring data and making it accessible, allowing users to search for information effectively. KGs also facilitate insights, inference, and reasoning. Traditional NLP methods, such as named entity recognition and relation extraction, are key in information retrieval but face limitations, including predefined entity types and the need for supervised learning. Current research leverages large language models’ capabilities, such as zero- or few-shot learning. However, unresolved and semantically duplicated entities and relations still pose challenges, leading to inconsistent graphs and requiring extensive post-processing. Additionally, most approaches are topic-dependent. In this paper, we propose iText2KG (The code and the dataset are available at https://***/AuvaLab/itext2kg), a method for incremental, topic-independent KG construction without post-processing. This plug-and-play, zero-shot method is applicable across a wide range of KG construction scenarios and comprises four modules: Documents Distiller, Incremental Entities Extractor, Incremental Relations Extractor, and Graph Integrator. Our method demonstrates superior performance compared to baseline methods across three scenarios: converting scientific papers to graphs, websites to graphs, and CVs to graphs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： knowledge graph

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 << < 38 39 40 41 42 43 44 45 46 47 > >>

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：