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

57,254 篇 会议
184 册 图书

馆藏范围

57,431 篇 电子文献
7 种 纸本馆藏

日期分布

学科分类号

57,042 篇 工学
- 56,774 篇 计算机科学与技术...
- 7,556 篇 控制科学与工程
- 2,463 篇 软件工程
- 2,057 篇 电气工程
- 483 篇 信息与通信工程
- 360 篇 生物医学工程（可授...
- 67 篇 机械工程
- 57 篇 土木工程
- 23 篇 石油与天然气工程
- 18 篇 生物工程
- 15 篇 仪器科学与技术
4,454 篇 理学
- 3,698 篇 数学
- 368 篇 物理学
- 274 篇 生物学
- 53 篇 科学技术史(分学科...
- 52 篇 统计学（可授理学、...
- 22 篇 系统科学
- 18 篇 化学
2,190 篇 医学
- 1,233 篇 基础医学(可授医学...
- 1,065 篇 临床医学
- 36 篇 特种医学
2,181 篇 文学
- 2,178 篇 外国语言文学
1,355 篇 管理学
- 1,256 篇 管理科学与工程(可...
- 84 篇 图书情报与档案管...
1,231 篇 教育学
- 709 篇 教育学
- 507 篇 心理学(可授教育学...
- 15 篇 体育学
57 篇 法学
- 56 篇 社会学
36 篇 艺术学
- 36 篇 音乐与舞蹈学
35 篇 哲学
- 35 篇 哲学
13 篇 经济学
1 篇 农学
1 篇 军事学

主题

1,258 篇 artificial intel...
1,112 篇 machine learning
978 篇 deep learning
919 篇 data mining
780 篇 multi agent syst...
725 篇 semantics
527 篇 classification
510 篇 reinforcement le...
474 篇 learning systems
463 篇 clustering
439 篇 neural networks
415 篇 ontology
353 篇 natural language...
257 篇 computers
249 篇 rough sets
236 篇 fuzzy sets
230 篇 feature selectio...
219 篇 computer circuit...
213 篇 knowledge repres...
205 篇 logic programmin...

机构

191 篇 univ chinese aca...
107 篇 chinese acad sci...
65 篇 harbin inst tech...
63 篇 polish acad sci ...
62 篇 wroclaw univ sci...
59 篇 soochow univ sch...
55 篇 delft univ techn...
53 篇 carnegie mellon ...
48 篇 beijing univ tec...
44 篇 chinese acad sci...
44 篇 shanghai jiao to...
43 篇 tu wien austria
42 篇 univ sci & techn...
41 篇 czech tech univ ...
40 篇 univ ulsan sch e...
39 篇 beijing jiaotong...
38 篇 tsinghua univ de...
38 篇 north carolina s...
37 篇 nanjing univ sta...
36 篇 tsinghua univ sc...

作者

47 篇 gelbukh alexande...
42 篇 wang lei
36 篇 reis luis paulo
35 篇 pelachaud cather...
35 篇 malerba donato
34 篇 liu yang
33 篇 novais paulo
33 篇 cpalka krzysztof
32 篇 li li
30 篇 ricci alessandro
27 篇 lau nuno
26 篇 wang hui
26 篇 napoli amedeo
25 篇 boryczka urszula
25 篇 rutkowski leszek
25 篇 wang jing
25 篇 pluhacek michal
25 篇 bickmore timothy
25 篇 calvo hiram
24 篇 villasenor-pined...

语言

57,321 篇 英文
116 篇 中文
1 篇 丹麦文

检索条件"丛书名=Lecture notes in artificial intelligence,"

共 57438 条记录，以下是151-160 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：

PqE: Zero-Shot Document Expansion for Dense Retrieval with Large Language Models 13th

PqE: Zero-Shot Document Expansion for Dense Retrieval with L...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Liu, Jiyuan Zou, Dongsheng Chai, Naiquan Yang, Yuming Wang, Hao Song, Xinyi Chongqing Univ Sch Comp Sci Chongqing Peoples R China

ISBN: (纸本)9789819794300;9789819794317

The dense retrieval model offers remarkable capabilities, yet it exhibits inconsistencies in the embedding space of queries and documents due to its dual-encoder structure. Addressing this limitation, we introduce Pseudo-query Embedding (PqE), a document expansion approach that eliminates the need for supervised data. By zero-shot prompting large language models (LLMs), we generate a specific number of pseudo-queries for each document, which are used to mitigate inconsistencies in the embeddings between queries and documents. This innovative strategy employs a multi-stage retrieval process to expand documents, enhancing the performance of the dense retrieval model without unduly impacting retrieval time. Experimental results demonstrate the efficacy of PqE. On the TREC DL dataset, PqE enhances the nDCG@10 metric of the unsupervised dense retrieval model Contriever by 7% points, and the Recall@1k metric by 4% points, surpassing the performance of the BM25 algorithm. Even for contrieverFT, fine-tuned on the massive dataset MS-MARCO, and BGE, trained on hundreds of millions of query-document pairs, PqE boosts their Recall@1k metrics on the TREC DL dataset by 1 to 2% points. Notably, on out-of-domain datasets from BEIR, PqE elevates the performance of most models across all metrics.

关键词： Dense retrieval models Large language models Document expansion

来源：评论

学校读者我要写书评

暂无评论

Instruct Large Language Models to Generate Scientific Literature Survey Step by Step 13th

Instruct Large Language Models to Generate Scientific Litera...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Lai, Yuxuan Wu, Yupeng Wang, Yidan Hu, Wenpeng Zheng, Chen Open Univ China Beijing Peoples R China Minist Educ Engn Res Ctr Integrat & Applicat Digital Learning Beijing Peoples R China FloatMiracle Beijing Peoples R China PLA Acad Mil Sci Beijing Peoples R China

ISBN: (纸本)9789819794423;9789819794430

Automatically generating scientific literature surveys is a valuable task that can significantly enhance research efficiency. However, the diverse and complex nature of information within a literature survey poses substantial challenges for generative models. In this paper, we design a series of prompts to systematically leverage large language models (LLMs), enabling the creation of comprehensive literature surveys through a step-by-step approach. Specifically, we design prompts to guide LLMs to sequentially generate the title, abstract, hierarchical headings, and the main content of the literature survey. We argue that this design enables the generation of the headings from a high-level perspective. During the content generation process, this design effectively harnesses relevant information while minimizing costs by restricting the length of both input and output content in LLM queries. Our implementation with Qwen-long achieved third place in the NLPCC 2024 Scientific Literature Survey Generation evaluation task, with an overall score only 0.03% lower than the second-place team. Additionally, our soft heading recall is 95.84%, the second best among the submissions. Thanks to the efficient prompt design and the low cost of the Qwen-long API, our method reduces the expense for generating each literature survey to 0.1 RMB, enhancing the practical value of our method.

关键词： Automatic literature survey Large language model Prompt design

来源：评论

学校读者我要写书评

暂无评论

An Iterative Framework for Document-Level Event Argument Extraction Assisted by Long Short-Term Memory 13th

An Iterative Framework for Document-Level Event Argument Ext...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： You, Tao Li, Zejun Fan, Zhihao Yin, Cunxiang Li, Yancheng Cao, Jing Fu, JinHua Wei, Zhongyu Fudan Univ Sch Data Sci Shanghai Peoples R China Tencent Shenzhen Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9789819794331;9789819794348

Document-level Event Argument Extraction (DEAE) is a highly challenging subtask of information extraction, especially in news scenarios where the event structure is complex. Most of the current methods are entity-based classification or generative frameworks, facing significant challenges when dealing with argument types that are not entities and handling complex event types. In this paper, we propose an iterative extraction framework for DEAE, which simulates human reading habits to iterate documents sentence by sentence. We utilize a long-term memory to effectively capture and utilize document context during iteration, compensating for the model's limited global information. To assist the model in understanding the complex events during iteration, the extracted arguments, considered short-term information, are used to enrich a dynamic prompt for extraction. Experiments on the news dataset DocEE demonstrate that our model outperforms previous methods. The ablation study also proves the effectiveness of each module.

关键词： document-level EAE iterative prompt learning long-short-term memory

来源：评论

学校读者我要写书评

暂无评论

Retrospective and Perspectives of TTS & STT Technology Development and Implementation for South Slavic Under-Resourced Languages 26th

Retrospective and Perspectives of TTS & STT Technology Devel...

引用

26th International Conference on Speech and Computer

作者： Secujski, Milan Popovic, Branislav Pekar, Darko Jakovljevic, Niksaa Pakoci, Edvin Suzic, Sinisaa Nosek, Tijana Simic, Nikola Stanojev, Vuk Delic, Vlado Univ Novi Sad Fac Tech Sci Novi Sad Serbia AlfaNum Ltd Novi Sad Serbia

ISBN: (纸本)9783031779602;9783031779619

Speech technologies such as text-to-speech (TTS) and speech-to-text (STT) are becoming increasingly applicable. Significant improvements in their quality are driven by advancements in deep machine learning. The ability of devices to deeply understand human speech and generate appropriate responses is a hallmark of AI capabilities. Developing speech technology requires extensive speech and language resources, which is why many languages with smaller speaker bases lag behind widely spoken languages in the development of speech technologys. Prior to the deep learning (DL) paradigm, hidden Markov models (HMM) and probabilistic approaches dominated speech technology development. This paper reviews the challenges and solutions in TTS and STT development for Serbian, highlighting the transition from HMM to DL. It also explores the future prospects of speech technology development for under-resourced languages and its role in preserving these languages.

关键词： Speech Technology Development and Implementation Text-to-Speech Speech-to-Text Hidden Markov Models Deep Neural Networks

来源：评论

学校读者我要写书评

暂无评论

Overview of the NLPCC 2024 Shared Task on Chinese Metaphor Generation 13th

Overview of the NLPCC 2024 Shared Task on Chinese Metaphor G...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Qu, Xingwei Zhang, Ge Wu, Siwei Li, Yizhi Lin, Chenghua Univ Manchester Manchester Lancs England Stardust AI Beijing Peoples R China Univ Waterloo Waterloo ON Canada Multimodal Art Project Res Community Beijing Peoples R China

ISBN: (纸本)9789819794423;9789819794430

This paper presents the results of the shared task on Chinese metaphor generation, hosted at the 13th CCF Conference on Natural Language Processing and Chinese Computing (NLPCC 2024). The goal of this shared task is to generate Chinese metaphors using machine learning techniques and effectively identifying basic components of metaphorical sentences. It is divided into two subtasks: 1) Metaphor Generation, which involves creating a metaphor from a provided tuple consisting of TENOR, GROUND, and VEHICLE. The goal here is to synthesize a metaphor that connects the subject (i.e. TENOR) with the object (i.e. VEHICLE), guided by the concept of the GROUND. 2) Metaphor Components Identification, which extracts the most fitting TENORs, GROUNDs, and VEHICLEs from a metaphorical sentence. This component requires the identification of the most fitting metaphor elements that correspond to the specified grounds. In addition to overall results, we report on the setup and insights from the metaphor generation shared task, which attracted a total of 4 participating teams across both subtasks.

关键词： metaphor generation metaphor components identification

来源：评论

学校读者我要写书评

暂无评论

Deep Foreground-Background Weighted Cross-modal Hashing 13th

Deep Foreground-Background Weighted Cross-modal Hashing

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Zhao, Guanqi Mao, Xan-Ling Tu, Rong-Cheng Ji, Wenjin Huang, Heyan Beijing Inst Technol Beijing Peoples R China

ISBN: (纸本)9789819794362;9789819794379

With the rapid growth of multi-modal data, deep cross-modal hashing algorithms provide a perfect solution for cross-modal retrieval tasks for their advantages of efficient retrieval speed and low storage consumption. Currently, the existing supervised cross-modal hashing methods, in order to efficiently extract structured information from raw data, generally gather on feature extraction of global information, however, all those methods ignore the weight differentiation between foreground and background information in a image. To address the issue, we propose a novel Deep Foreground-Background Weighted Cross-Modal Hashing(DFBWH) for supervised cross-modal retrieval. Specifically, the proposed method firstly performs target detection on the original image and select out candidate regions as target foreground entities. Then, the proposed method utilize the semantic interactions in the textual descriptions and tagging information as evaluation criteria, and use CLIP to detect the matching degree of the candidate regions. Eventually, under the supervision of the category labeling information, the hash loss function is utilized to obtain a high-quality hash code. Extensive experiments were carried out on two benchmark datasets, which demonstrate that DFBWH achieves better performance than the state-of-the-art baselines.

关键词： Cross-modal retrieval Foreground-Background Weighted Deep hashing

来源：评论

学校读者我要写书评

暂无评论

Enhancing Chinese Essay Discourse Logic Evaluation Through Optimized Fine-Tuning of Large Language Models 13th

Enhancing Chinese Essay Discourse Logic Evaluation Through O...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Song, Jinwang Song, Yanxin Zhou, Guangyu Fu, Wenhui Zhang, Kunli Zan, Hongying Zhengzhou Univ Zhengzhou Peoples R China

ISBN: (纸本)9789819794423;9789819794430

Due to the high complexity and diversity of writing, automated essay evaluation systems face significant challenges. Large language models (LLMs), representing the latest peak in NLP technology for semantic understanding, hold immense potential for advancing essay evaluation systems. In the NLPCC 2024 Shared Task 4 Chinese Essay Discourse Logic Evaluation and Integration, we investigated improving LLMs' capabilities in evaluating essay logic, coherence, and quality. Considering the characteristics of different tasks, we adopted MRC-style instructions to optimize output formats and implemented undersampling to address data imbalance. To enhance efficiency and model performance, we explored LLM fine-tuning methods that decouple tasks and applied similarity comparison to refine model outputs. Additionally, we utilized noisy embedding fine-tuning to mitigate overfitting. Our approach achieved the top ranking in the NLPCC 2024 Shared Task 4.

关键词： Essay Evaluation Large Language Models Natural Language Processing

来源：评论

学校读者我要写书评

暂无评论

Hate Speech Detection for the Power Domain 13th

Hate Speech Detection for the Power Domain

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Huang, Qingbao Deng, Zehua Chen, Shizhen Chen, Yifei Shuang, Feng Guangxi Univ Sch Elect Engn Nanning Guangxi Peoples R China Guangxi Univ Sch Comp Elect & Informat Nanning Guangxi Peoples R China Guangxi Key Lab Intelligent Control & Maintenance Nanning Guangxi Peoples R China

ISBN: (纸本)9789819794393;9789819794409

The professional nature and confidentiality of the power domain hinder the public to accurately assess the authenticity of online electricity-related statements, fostering an environment conducive to the spread of electricity-related hate speech on social media. To address this challenge, we introduce a new hate speech detection task for the electric power domain. A dataset for electric power domain hate speech detection is constructed, consisting of 6000 electricity-related Weibo posts. We propose a prompt learning approach for hate speech detection in the electric power domain, which integrates power domain knowledge, such as work scenarios and terms. Subsequently, a prompt template is formulated to facilitate hate speech detection. Experimental results on the dataset indicate that the proposed prompt learning method surpasses the baseline model.

关键词： Hate Speech Detection Prompt Learning Pre-trained visual language model

来源：评论

学校读者我要写书评

暂无评论

NLPCC2024 Shared Task 3 Technical Report 13th

NLPCC2024 Shared Task 3 Technical Report

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Sun, Yingfei Ji, Wei Natl Univ Singapore Singapore Singapore

This technical report presents our approach for the NLPCC 2024 Shared Task 3. It outlines the task content, and our proposed methodology and introduces some related works of this task. The report provides a brief desc...

ISBN: (纸本)9789819794423;9789819794430

关键词： Dialogue Relation Extraction Coreference Resolution

来源：评论

学校读者我要写书评

暂无评论

Regularized Continual Learning for Large-Scale Language Models via Probing 13th

Regularized Continual Learning for Large-Scale Language Mode...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Song, Xingshen Ren, Tianxiang Deng, Jinsheng Natl Univ Def Technol Changsha 410073 Peoples R China

ISBN: (纸本)9789819794362;9789819794379

The rapid development of large-scale language models has garnered widespread interest from both academia and industry. Efficiently applying those models across various domains is now posing a challenge to researchers. High training costs and the relative scarcity of domain-specific data have rendered continual learning on general pretrained language models as one preferable approach. In this paper, we provide a comprehensive analysis and modification of these continual learning strategies for large language models, as they were initially designed for encoderonly architectures. Then a probing algorithm for the token representation shift was proposed to better alleviate forgetting. Additionally, corresponding evaluation metrics were modified for quantitative analysis of our methods. Through the experiment across three different domains, we verified the effectiveness of continual learning and probing algorithms on recent models. Results showed that knowledge distillation outperforms other methods in cross-domain continual learning. Moreover, the introduction of probing can further enhance the accuracy with a relatively small calculation budget.

关键词： Large-scale language model Continual learning Probing Classifier

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 << < 12 13 14 15 16 17 18 19 20 21 > >>

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：