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211-220 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：

Bias-Rectified Multi-way Learning with Data Augmentation for Implicit Discourse Relation Recognition 13th

Bias-Rectified Multi-way Learning with Data Augmentation for...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Zheng, Ziwei Liang, Chao Xiang, Wei Wang, Bang Huazhong Univ Sci & Technol Sch Elect Informat & Commun Hubei Key Lab Smart Internet Technol Luoyu Rd 1037 Wuhan 430074 Hubei Peoples R China Cent China Normal Univ Fac Artificial Intelligence Educ Wuhan Peoples R China

ISBN: (纸本)9789819794300;9789819794317

Implicit Discourse Relation Recognition (IDRR) is a challenging but vital task in discourse analysis that focuses on identifying and classifying the relation between two arguments without explicit connectives. Previous research has focused on constructing sophisticated argument representations or utilizing labels' hierarchical information while neglecting the intrinsic prior relational bias, where some relation categories are significantly less frequent than others due to inherent linguistic properties. Furthermore, some works amalgamate Explicit Discourse Relation Recognition (EDRR) data with IDRR data to achieve data augmentation;however, the linguistic discrepancies between EDRR and IDRR data may mislead the relation recognition process. To address the prior relation bias and misleading from linguistic discrepancies, we propose a novel Bias-REctified Multi-way Learning (BREM) model, which incorporates prior probability knowledge to assist in understanding the discourse relations and employs a PLM-based method for pre-training on the EDRR dataset to achieve data augmentation without introducing misleading effects. Experiments on the PDTB 3.0 corpus show that BREM outperforms previous models, especially in identifying less frequent relation senses, highlighting the effectiveness of our proposed model. The source code of our proposed model is publicly available at https://***/zzzziwy/NLPCC2024-BREM_for_IDRR.

关键词： Implicit Discourse Relation Recognition prior relation bias Data Augmentation

来源：评论

学校读者我要写书评

暂无评论

TKGR-GPRSCL: Enhance Temporal Knowledge Graph Reasoning with Graph Structure-Aware Path Representation and Supervised Contrastive Learning 13th

TKGR-GPRSCL: Enhance Temporal Knowledge Graph Reasoning with...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Xiong, Lizhu Sheng, Yongpan He, Lirong Southwest Univ Coll Comp & Informat Sci Chongqing 400715 Peoples R China Chongqing Univ Sch Big Data & Software Engn Chongqing 401331 Peoples R China Chongqing Jiaotong Univ Sch Informat Sci & Engn Chongqing 400074 Peoples R China

ISBN: (纸本)9789819794331;9789819794348

Temporal knowledge graph reasoning (TKGR) aims to infer new temporal fact knowledge based on existing ones, which has become a major concern due to its wide demands, especially for time-sensitive modeling scenarios. However, existing temporal knowledge graph representation learning models usually have challenges in capturing complex graph structure-aware information and discriminating different relation expressions for unknown query prediction, which potentially limits the model performance on the TKGR task. In this paper, we propose a novel TKGR model named TKGR-GPRSCL. Specifically, we first introduce a maximum entropy-based random walk sampler, which can sample sufficient paths containing complicated graph structure-aware information in the TKG in form of path embedding. Then, we model the preference of different paths to the target entity, and the correlations between the target entity and its temporal property depending on transformations in the complex plane for encoding temporal fact. Finally, we advance supervised contrastive learning of relation patterns in light of the discrimination of different relation expressions to be preferably leveraged to predict entities for testing the queries, for those unknown queries in particular. We evaluate TKGR-GPRSCL on three TKGR benchmark datasets, and experimental results demonstrate that our model achieves superior performance compared to competitive baselines (All the data and codes are publicly available at https://***/shengyp/TKGR-GPRSCL).

关键词： Temporal knowledge graph reasoning Graph structure-aware path representation Supervised contrastive learning

来源：评论

学校读者我要写书评

暂无评论

Activate Integrated Controllable Generation with Soft Prompt 13th

Activate Integrated Controllable Generation with Soft Prompt

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Ma, Jingkun Zhan, Runzhe Wong, Derek F. Chao, Lidia S. Univ Macau Dept Comp & Informat Sci NLP2CT Lab Macau Peoples R China

ISBN: (纸本)9789819794393;9789819794409

Parameter-efficient transfer learning (PETL) methods have gained significant adoption in downstream tasks due to their ability to reduce the cost of tuning pre-trained language models. However, a tradeoff between performance and efficiency remains. However, controllable Text Generation (CTG) requires a precise understanding of diverse constraints to mitigate potential degradation in generation quality. In contrast to single-attribute CTG, multi-attribute CTG amplifies the tuning complexity for PETL methods. To address this challenge, we propose Activator, a PETL approach that accommodates CTG tasks with higher diversity and offers fine-grained control. Activator leverages an external module to enhance optimization and enriches the soft prompt representations. Our experimental results on table-to-text and poetry generation tasks demonstrate that Activator exhibits remarkable competitiveness compared to other PETL methods when applied to both casual language model and sequence-to-sequence language models. Furthermore, we observe that Activator demonstrates strong performance even in extremely complex CTG scenarios. The source code is publicly available at https://***/NLP2CT/Activator.

关键词： Controllable Generation Prompt Parameter-Efficient

来源：评论

学校读者我要写书评

暂无评论

Graph Interpretation of Image-Text Matching: Link Prediction on Concept-Enhanced Cross-Modal Graph 13th

Graph Interpretation of Image-Text Matching: Link Prediction...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Fan, Zhihao Li, Zejun Wang, Siyuan Wei, Zhongyu Shan, Haijun Fudan Univ Shanghai Peoples R China CEC GienTech Technol Co Ltd Shanghai Peoples R China

ISBN: (纸本)9789819794362;9789819794379

Image-text retrieval aims to retrieve semantically related samples in a different modality given text/image as input. Previous works focus on developing cross-model interaction for the local relationship of the image-text pair to determine the matching. We argue that the paradigm overlooks the global relationship with other candidate texts. The limited information of a single sentence may fails to link sentences and images, and the lack of reference and comparison in globalness leads to the modeling being dominated by the local false signal. We propose to include contextual texts and images to enhance the semantic modeling of an image-text pair. Motivated by the success of graph model for neighbourhood information modeling, we build a cross-modal graph and formulate image-text retrieval task as link prediction on the graph. We add textual concepts beyond image and text in the cross-modal graph to better clarify the global relationship. In the link prediction for matching determination, we propose PAth based Transformer (PAT) or the simultaneous modeling of both semantics and structure. In experiments, we verify the effectiveness of our model on MS-COCO and Flickr30K compared with current competitive pre-trained models.

关键词： Image-Text Retrieval Multimodal Learning Fine-Grained Cross-Model Alignment

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Knowledge Aggregation for Personalized Response Generation in Dialogue Systems 13th

Hierarchical Knowledge Aggregation for Personalized Response...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Dong, Yuezhou Qin, Ke Liang, Shuang Univ Elect Sci & Technol China Natl Key Lab Intelligent Collaborat Comp Chengdu 611731 Sichuan Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Sichuan Peoples R China

ISBN: (纸本)9789819794300;9789819794317

Personalized Dialogue Response Generation, which aims to emulate human-like and customized responses, has attracted considerable research interest. Recent advances in this field have integrated external knowledge to enhance models' language comprehension. However, the generic nature of this external knowledge is inadequate for the generation of personalized responses for a diverse range of users. Moreover, the redundancy of external knowledge diverts models' attention to other topics. To address these challenges, we propose a novel GNN-based approach, Hierarchical Knowledge Aggregation (HKA), which hierarchically aggregates both external knowledge and knowledge from the dialogue context. Additionally, we introduce a dialogue relation recognition task to enhance the accurate modeling of structural information. The results of our experiments, conducted on the ConvAI2 and PersonalDialog datasets, demonstrate that HKA outperforms existing baselines in generating personalized responses.

关键词： Personalized Dialogue Systems Knowledge Graph Graph Neural Network

来源：评论

学校读者我要写书评

暂无评论

Contrastive Learning-Based Sequential Recommendation Model 13th

Contrastive Learning-Based Sequential Recommendation Model

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Zhang, Yuan Nuo, Minghua Jia, Xiaoyu Wang, Yao Inner Mongolia Univ Coll Comp Sci Hohhot 010021 Inner Mongolia Peoples R China Natl & Local Joint Engn Res Ctr Intelligent Infor Hohhot 010021 Inner Mongolia Peoples R China Inner Mongolia Key Lab Multilingual Artificial In Hohhot 010021 Inner Mongolia Peoples R China

ISBN: (纸本)9789819794393;9789819794409

Contrastive learning has demonstrated remarkable performance in sequential recommendation tasks. The core principle of it is to maximize consistency between the original and augmented data while increasing the distance between the original data and other instances, thereby facilitating the learning of more discriminative features. Nonetheless, such an approach has limitations and may inadvertently augment the distance between samples that belong to the same category in the feature space. To tackle the above problems, we propose a novel contrastive learning paradigm for sequential recommendation, termed CLSRec (Contrastive Learning-Based Sequential Recommendation Model). Our framework integrates cross-domain and intra-domain features through a meticulously designed contrastive loss function and introduces gray-scale positive sampling, aiming to address the inadequacy of focus on similar items in sequential recommendation. The proposed contrastive learning framework effectively captures intra-sequence item transition patterns and inter-sequence dependencies among items. Empirical evaluations on real-world datasets show that our model significantly outperforms advanced baseline models, validating its effectiveness in sequential recommendation scenarios.

关键词： Sequential Recommendation Contrastive Learning Graph Neural Networks Collaborative Filtering

来源：评论

学校读者我要写书评

暂无评论

Bulgarian Speech Resources in the CHILDES System 26th

Bulgarian Speech Resources in the CHILDES System

引用

26th International Conference on Speech and Computer

作者： Popova, Velka Popov, Dimitar Konstantin Preslavska Univ Shumen Univ Str 115 Shumen 9700 Bulgaria

ISBN: (纸本)9783031779602;9783031779619

The main aim of the article is to present the Bulgarian speech resources in the CHILDES automated computerized language data exchange system. The resources are organized into a corpus labeled Bulgarian LabLing Corpus (https://***/access/Slavic/Bulgarian/LabLing. html). This database is the most recent contribution to the CHILDES Slavic collection, the first version of which was published in 2020. It covers longitudinal data from 5 young children and the picture-based narratives of 121 preschoolers. The focus in the first Bulgarian electronic corpus with children's speech is on the possibilities for the study of child-adult speech interaction, which is extremely important for the adequate modeling of language ontogenesis.

关键词： Child speech resources CHILDES Bulgarian LabLing corpus

来源：评论

学校读者我要写书评

暂无评论

Research on Named Entity Recognition in Ancient Chinese Based on Incremental Pre-training and Domain Lexicon 13th

Research on Named Entity Recognition in Ancient Chinese Base...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Kang, Wenjun Zuo, Jiali Dai, Qili Hu, Yiyu Wang, Mingwen Jiangxi Normal Univ Sch Comp & Informat Engn Nanchang Jiangxi Peoples R China Nanchang Inst Technol Sch Comp Informat Engn Nanchang Jiangxi Peoples R China Guangzhou Inst Sci & Technol Sch Comp Sci & Engn Guangzhou Peoples R China

ISBN: (纸本)9789819794300;9789819794317

Currently, there is limited research on ancient Chinese named entity recognition, primarily due to the scarcity of publicly available datasets for model training. We constructed a CMAG-NER dataset based on the "Comprehensive Mirror for Aid in Government". Addressing the challenges faced by existing models in identifying person entities with omitted surnames and determining entity boundaries in ancient Chinese texts, we integrated the LEBERT-CRF model with a general domain lexicon and the "Comprehensive Mirror for Aid in Government Dictionary" to fuse external statistical information and rule-based knowledge, thereby enhancing the performance of ancient Chinese named entity recognition. Additionally, to improve the model's comprehension of ancient Chinese, we have compiled a substantial corpus of ancient Chinese literature for incremental pre-training of BERT-Ancient-Chinese. Experimental results demonstrate that the proposed method effectively mitigates the aforementioned challenges, but the performance of large models on this task still requires improvement.

关键词： Chinese Named Entity Recognition Data Annotation Domain Knowledge Large Language Models

来源：评论

学校读者我要写书评

暂无评论

A Cross-Modal Correlation Fusion Network for Emotion Recognition in Conversations 13th

A Cross-Modal Correlation Fusion Network for Emotion Recogni...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Tang, Xiaolyu Cai, Guoyong Chen, Ming Yuan, Peicong Guilin Univ Elect Technol Guilin Peoples R China

ISBN: (纸本)9789819794423;9789819794430

The aim of Emotion Recognition in Conversations (ERC) is to predict the emotions conveyed in the utterances of a conversation. In this paper, we propose a Cross-Modal Correlation Fusion Network (CMCFN), which addresses the limitations of existing approaches to exploit correlations across multiple modalities and the difficulty of classifying tail emotion categories. The proposed Cross-Modal Correlation Encoder (CMCE) effectively models intricate cross-modal correlations in a conversation, facilitating efficient multimodal fusion. In addition, the designed Multimodal Contrastive Representation Learning Network (MCRLN) mitigates the difficulty in categorizing tail emotions by combining supervised contrastive learning and multimodal data augmentation. Experimental results on the IEMOCAP and MELD datasets demonstrate the effectiveness and superiority of our proposed CMCFN model.

关键词： Emotion Recognition in Conversations Multimodal fusion Cross-modal correlations

来源：评论

学校读者我要写书评

暂无评论

FacGPT: An Effective and Efficient Method for Evaluating Knowledge-Based Visual Question Answering 13th

FacGPT: An Effective and Efficient Method for Evaluating Kno...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Cheng, Sirui Zhang, Siyu Wu, Jiayi Lan, Muchen Sun, Yaoru Tongji Univ Dept Comp Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9789819794300;9789819794317

Enhancing the accuracy of model evaluation accelerates the subsequent optimization process of large visual language models (LVLMs). However, evaluating the diversity of LVLM outputs is challenging. The long-form responses provided by LVLM are susceptible to interference from visual facts, which mainly manifests as factual inaccuracies stemming from not following the given image instructions. This problem makes the traditional Exact Match evaluation method no longer satisfied with measuring the correct expression of LVLM. To this end, this paper proposes KVQA-Eval, a novel evaluation benchmark aiming to reflect the discriminative ability of LVLM in the knowledge VQA domain. We propose a dataset (40k) called K-VQA, which improves the evaluation of answer correctness by incorporating human annotations. Furthermore, we train a scalable and deterministic FacGPT evaluator model based on GPT2. FacGPT not only achieves better correlation with human evaluators and GPT4 but also improves evaluation efficiency by 300%. More encouragingly, this evaluation model will help promote the development of automated evaluation tools due to their data safety, cost-effectiveness, and reproducibility. Our open-source code, dataset, and model at https://***/Siri-2001/FacGPT.

关键词： LVLM Knowledge VQA Evaluation benchmark

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 << < 18 19 20 21 22 23 24 25 26 27 > >>

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：