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61-70 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：

Nominal Compound Chain Extraction Enhanced by Chain-of-Thought Information 13th

Nominal Compound Chain Extraction Enhanced by Chain-of-Thoug...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Li, Chenyang Zhang, Long Guo, Hui Zheng, Qiusheng Cent China Normal Univ Fac Artificial Intelligence Educ Wuhan Peoples R China Zhongyuan Univ Technol Sch Cyberspace Secur Zhengzhou Peoples R China

ISBN: (纸本)9789819794423;9789819794430

In traditional lexical chain extraction tasks, researchers typically focus on identifying simple lexical items based on surface grammatical relations, often overlooking compound words with underlying semantic frameworks. To address this limitation, the task of Nominal Compound Chain Extraction (NCCE) has emerged. This task aims to identify and cluster nominal compounds sharing the same semantic theme, thereby providing richer semantic information and facilitating a deeper understanding of the latent themes within documents. In this study, we fine-tune the large language model Qwen2-0.5b, employ data augmentation techniques, and introduce Chain-of-Thought (CoT) information from large models as an auxiliary aid, significantly enhancing the model's document comprehension capabilities.

关键词： Nominal Compound chain Large Language Model Data Augmentation Chain-of-Thought

来源：评论

学校读者我要写书评

暂无评论

YoloGPT: Enhancing Chinese Character Recognition and Correction 13th

YoloGPT: Enhancing Chinese Character Recognition and Correct...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Yang, Sheng Lian, Zhanbiao Li, Kunyu Liu, Peilin Wu, Fengge Zhao, Junsuo Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Inst Software Beijing Peoples R China

ISBN: (纸本)9789819794423;9789819794430

Helping people to write well is a fundamental area of Natural Language Processing (NLP) research. The purpose of writing assistance is to improve the accuracy and quality of the input text, with character checking being one of the essential abilities to detect and correct erroneous characters. The erroneous characters appear in humans' handwriting include faked characters (nonexistent characters due to handwriting errors) and misspelled characters (incorrect use of real characters due to spelling errors). In this paper, we introduce YoloGPT, a novel model that combines YOLO and GPT for detecting and correcting erroneous characters within visual form human written texts in real-world scenarios, including faked and misspelled Chinese characters. YoloGPT begins by using a YOLO object detection model customized to recognize character sets to form sentences with Chinese characters and label the faked ones. Then, it employs the powerful large language model GPT equipped with an automatic prompt optimization method specially designed to find an optimized prompt. The LLMs use the explicit guidance of the prompt to correct the erroneous characters and generate the final answer. The YoloGPT model has achieved remarkable results, securing first place in Task 1 of NLPCC2024, demonstrating the effectiveness of our method.

关键词： Chinese Character Checking YOLO Large Language Models

来源：评论

学校读者我要写书评

暂无评论

Integrating Multi-view Analysis: Multi-view Mixture-of-Expert for Textual Personality Detection 13th

Integrating Multi-view Analysis: Multi-view Mixture-of-Exper...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Zhu, Haohao Zhang, Xiaokun Lu, Junyu Yang, Liang Lin, Hongfei Dalian Univ Technol Sch Comp Sci & Technol Dalian Peoples R China

ISBN: (纸本)9789819794393;9789819794409

Textual personality detection aims to identify personality traits by analyzing user-generated content. To achieve this effectively, it is essential to thoroughly examine user-generated content from various perspectives. However, previous studies have struggled with automatically extracting and effectively integrating information from multiple perspectives, thereby limiting their performance on personality detection. To address these challenges, we propose the Multi-view Mixture-of-Experts Model for Textual Personality Detection (MvP). MvP introduces a Multi-view Mixture-of-Experts (MoE) network to automatically analyze user posts from various perspectives. Additionally, it employs User Consistency Regularization to mitigate conflicts among different perspectives and learn a multi-view generic user representation. The model's training is optimized via a multi-task joint learning strategy that balances supervised personality detection with self-supervised user consistency constraints. Experimental results on two widely-used personality detection datasets demonstrate the effectiveness of the MvP model and the benefits of automatically analyzing user posts from diverse perspectives for textual personality detection.

关键词： personality detection mixture-of-experts consistency regularization

来源：评论

学校读者我要写书评

暂无评论

Investigating the Utility of wav2vec 2.0 Hidden Layers for Detecting Multiple Sclerosis 26th

Investigating the Utility of wav2vec 2.0 Hidden Layers for D...

引用

26th International Conference on Speech and Computer

作者： Gosztolya, Gabor Toth, Laszlo Svindt, Veronika Bona, Judit Hoffmann, Ildiko HUN REN SZTE Res Grp Artificial Intelligence Szeged Hungary Univ Szeged Inst Informat Szeged Hungary HUN REN Res Ctr Linguist Budapest Hungary Eotvos Lorand Univ Dept Linguist Budapest Hungary Univ Szeged Dept Psychiat Szeged Hungary

ISBN: (纸本)9783031779602;9783031779619

Multiple sclerosis (MS) is a chronic autoimmune neurodegenerative disease, affecting the central nervous system. The disease can induce various symptoms, such as adversarily affecting the speech of the subject in various ways, therefore allowing the use of automatic speech analysis for the detection of MS and for monitoring the condition of the patient. Owing to data scarcity, however, deep neural networks are usually not employed for this task as classifiers, but are used as feature extractors. This is the case for self-supervised networks such as wav2vec 2.0 as well, where a straightforward source of embeddings (used as features) are the last layers of the convolutional (lower) and fine-tuned (higher) blocks. In this study we investigate whether extracting the embeddings from some other, inner layer of the fine-tuned (transformer) block can help improve MS detection performance. Tested on two speech tasks, we found that the lowest one-third of the 24 fine-tuned layers proved to be the most suitable for feature extraction, which led to statistically significant improvements in the AUC scores for both speech tasks.

关键词： Multiple sclerosis . Pathological speech processing . Wav2vec 2.0 . Feature extraction

来源：评论

学校读者我要写书评

暂无评论

WConF: Weighted Contrastive Fusion for Multimodal Sentiment Analysis 13th

WConF: Weighted Contrastive Fusion for Multimodal Sentiment ...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Zeng, Biqing Li, Ruiyuan Lu, Liuxing Lu, Liangqi Wang, Jiazhen Chen, Weihai Dong, Huimin South China Normal Univ Sch Software Foshan Peoples R China South China Normal Univ Aberdeen Inst Data Sci & Artificial Intelligence Foshan Peoples R China South China Normal Univ Sch Econ & Management Guangzhou Peoples R China Guangdong AIB Polytech Sch Comp Sci Guangzhou Peoples R China

ISBN: (纸本)9789819794423;9789819794430

With the continuous development of multimedia, Multimodal Sentiment Analysis has become a highly regarded field. Recent research proposes learning effective unimodal representations to facilitate multimodal fusion, which mainly contain two parts of information: modality-common and modality-specific information. However, previous work does not consider the sentiment span information between different samples during the fusion process. In this paper, we propose a novel framework Weighted Contrastive Fusion (WConF) to extract sentiment span information for multimodal fusion. First, we apply modality contrastive learning to capture modality-common sentiment information and separate modality-specific sentiment information. Then, considering sentiment spans as weights, we perform weighted contrastive learning on the multimodal representations. Moreover, we design a loss function to assist in weighted contrastive fusion. Finally, we conduct extensive experiments on four benchmark datasets: MOSI, MOSEI, CH-SIMS, and CH-SIMSV2. Experimental results demonstrate that our model achieves superior performance compared to previous models.

关键词： Weighted Contrastive Fusion Multimodal Sentiment Analysis Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Cross-Cultural Automatic Depression Detection Based on Audio Signals 26th

Cross-Cultural Automatic Depression Detection Based on Audio...

引用

26th International Conference on Speech and Computer

作者： Marnontov, Danila Zepf, Sebastian Karpov, Alexey Minker, Wolfgang Ulm Univ Ulm Germany ITMO Univ St Petersburg Russia Russian Acad Sci St Petersburg Inst Informat & Automat St Petersburg Fed Res Ctr Russian Acad Sci SPC RAS St Petersburg Russia

ISBN: (纸本)9783031779602;9783031779619

Depression is a frequently occurring mental health disorder globally, and early detection is critical for effective treatment. In this paper, we explore the effectiveness of machine learning techniques in cross-cultural depression detection using audio signals from Chinese and English-speaking populations. We investigate the influence of temporal context length, feature sets, and classifiers on classification performance across two single and two cross-corpus settings. Our results show that hand-crafted features offer advantages in single and combined dataset settings, while deep learning-based features, particularly from emotion recognition tasks, demonstrate superior cross-dataset generalization. The optimal length of a temporal context strongly depends on the specific dataset. These findings highlight the importance of considering dataset-specific characteristics and feature selection in developing reliable and culturally adaptable models for depression detection. In the cross-corpus settings on MENHIR and CMDC datasets, we obtained the best F1 scores of 0.77 and 0.63, respectively. Future research should focus on enhancing model performance and data accessibility to ensure effective inclusion across diverse populations, ultimately contributing to better mental health outcomes globally.

关键词： Depression recognition Deep embeddings OpenSMILE Cross-corpus analysis CMDC MENHIR

来源：评论

学校读者我要写书评

暂无评论

How to Detect Imbalances in the Google Books Ngram Corpus? 26th

How to Detect Imbalances in the Google Books Ngram Corpus?

引用

26th International Conference on Speech and Computer

作者： Solovyev, Valery Ivleva, Anna Kazan Fed Univ Inst Philol & Intercultural Commun Lab Linguist & AI Kazan Russia

ISBN: (纸本)9783031780134;9783031780141

The Google Books Ngram diachronic corpus is an important tool to study changes in the language and society. The article draws attention to the problem of diachronic balance instability of various text genres in the corpus. The instability is shown not to correspond the publishing policy of the country. The method for detecting the imbalance is proposed. The Russian subcorpus is analyzed in detail to shows the presence of serious balance instabilities in the 21st century. We analyze the frequency dynamics of small groups of words, as well as make extended statistical research of the corpus. We calculate such parameters of word frequency dynamics as autocorrelations, linear trends, mathematical expectations and variances. We also apply the methods of singular spectral analysis and moving average to find unnatural peaks of the average "noise". Our conclusion is unreasonably large amount of fiction in the Russian subcorpus in some specific years. Similar effects are also found in the English subcorpus. These facts seem to be significant because the most often studied objects of psychological and sociological research are the high-frequency common words widespread in fiction. An example is given to demonstrate that ignoring this factor can result in erroneous conclusions.

关键词： the Google Books Ngram corpus diachronic balance instability imbalance of genres word frequency time series

来源：评论

学校读者我要写书评

暂无评论

Model-Agnostic Knowledge Distillation Between Heterogeneous Models 13th

Model-Agnostic Knowledge Distillation Between Heterogeneous ...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Shen, Jiaxin Liu, Yanyao Jiang, Yong Chen, Yufeng Han, Wenjuan Beijing Jiaotong Univ Beijing Key Lab Traff Data Anal & Min Beijing Peoples R China Alibaba DAMO Acad Hangzhou Peoples R China

ISBN: (纸本)9789819794300;9789819794317

The goal of KD is to transfer valuable knowledge from a strong teacher model to a weaker student model in order to bridge the performance gap. However, the conventional teacher-student paradigm of KD can not be applied to the teacher model and student model with different output representations (referred to as heterogeneous models). To overcome this limitation, we propose a model-agnostic approach for KD called MAKD. MAKD consists of two stages: informative sample generation and knowledge transfer. The key idea is to generate informative samples that convey the discrepancy between the teacher model and student model, so that the generated samples are effective and efficient. We formulate two ways for informative sample generation: word substitution and text generation. After generating desired samples, we perform knowledge transfer by mixing the informative samples into the original training set and retraining the student model to improve its performance. We conducted experiments on two tasks, dependency parsing, and grammatical error correction, and the results demonstrate that MAKD successfully enhances the performance of the student model on both tasks. Our data and code are available at https://***/WinnieHAN/Model_Agnostic_Knowledge_Distillation.

关键词： Knowledge Distillation Data Augmentation Model Agnostic

来源：评论

学校读者我要写书评

暂无评论

Multilingual Temporal Answer Grounding in Video Corpus with Enhanced Visual-Textual Integration 13th

Multilingual Temporal Answer Grounding in Video Corpus with ...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Ma, Tianxing Hu, Yueyue Jiang, Shuang Yin, Zhenhao Zang, Tianning Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Chinese Acad Sci Inst Informat Engn Beijing Peoples R China

ISBN: (纸本)9789819794423;9789819794430

This paper presents the experiment scheme of Team IIEleven in the NLPCC 2024 shared task, Multilingual Medical Instructional Video Question Answering (MMIVQA) challenge, Track 3: Multilingual Temporal Answer Grounding in Video Corpus (mTAGVC). The objective of the mTAGVC task is to identify video spans most relevant to the given questions from a large multilingual medical instructional video corpus. We propose an Multilingual Visual-Textual Span Enhancement (MVTSE) method, which simultaneously performs two subtasks of video corpus retrieval and temporal answering grounding in video, and further strengthens the cross language ability and comparative learning performance of the model by subtitle text supplementation, multilingual data augmentation and hard sample selection method. The experimental results on the given dataset show that the proposed method achieves state-of-the-art (SOTA) performance, and ranks first in the mTAGVC track of the MMIVQA challenge.

关键词： Multimodal Multilingual Temporal Answer Grounding Video Retrieval

来源：评论

学校读者我要写书评

暂无评论

Prompt-Based Joint Contrastive Learning for Zero-Shot Relation Extraction 13th

Prompt-Based Joint Contrastive Learning for Zero-Shot Relati...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Zou, Jianjian Xiao, Yuhui Zhou, Sichi Li, Wei Yang, Qun Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China Nari Grp Corp State Grid Elect Power Res Inst Nanjing Peoples R China

ISBN: (纸本)9789819794300;9789819794317

Zero-shot relation extraction is proposed to address the issue that the model performance drops dramatically when identifying novel relations that cannot be observed at the training stage. Existing works focus more on the pattern of matching between instances and relation descriptions, however, they ignore the capabilities of pre-trained model in zero-shot scenarios. In this paper, we propose a novel prompt-based joint contrastive learning framework (PCL) to enhance zero-shot relation extraction. Specifically, we construct a prompt template to leverage prior knowledge in PLMs for better representation of the relation. In order to learn more effective features, we design two contrastive learning modules to jointly optimize the obtained representations. Experimental results on two publicly available datasets show that PCL outperforms the previous methods.

关键词： zero-shot relation extraction prompt learning contrastive learning

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 << < 3 4 5 6 7 8 9 10 11 12 > >>

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：