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

26,340 篇 会议
405 册 图书
370 篇 期刊文献
3 篇 学位论文
1 篇 资讯

馆藏范围

27,118 篇 电子文献
2 种 纸本馆藏

日期分布

学科分类号

16,470 篇 工学
- 15,081 篇 计算机科学与技术...
- 11,258 篇 软件工程
- 2,788 篇 信息与通信工程
- 1,768 篇 控制科学与工程
- 1,560 篇 电气工程
- 1,409 篇 生物工程
- 625 篇 生物医学工程（可授...
- 610 篇 化学工程与技术
- 558 篇 机械工程
- 514 篇 电子科学与技术（可...
- 478 篇 网络空间安全
- 397 篇 仪器科学与技术
- 371 篇 光学工程
- 277 篇 安全科学与工程
5,078 篇 理学
- 2,071 篇 数学
- 1,472 篇 物理学
- 1,468 篇 生物学
- 780 篇 统计学（可授理学、...
- 620 篇 系统科学
- 617 篇 化学
4,712 篇 管理学
- 3,157 篇 图书情报与档案管...
- 1,828 篇 管理科学与工程(可...
- 766 篇 工商管理
835 篇 医学
- 610 篇 临床医学
- 569 篇 基础医学(可授医学...
- 392 篇 公共卫生与预防医...
- 250 篇 药学(可授医学、理...
681 篇 法学
- 599 篇 社会学
572 篇 文学
- 400 篇 外国语言文学
449 篇 教育学
- 438 篇 教育学
251 篇 经济学
177 篇 农学
19 篇 艺术学
17 篇 军事学
1 篇 哲学

主题

5,645 篇 natural language...
1,716 篇 natural language...
1,542 篇 semantics
1,442 篇 natural language...
1,195 篇 sentiment analys...
1,190 篇 machine learning
1,134 篇 deep learning
944 篇 accuracy
909 篇 training
800 篇 data mining
782 篇 feature extracti...
771 篇 computational mo...
755 篇 artificial intel...
544 篇 knowledge engine...
518 篇 speech recogniti...
503 篇 large language m...
490 篇 transformers
479 篇 data models
461 篇 information retr...
460 篇 support vector m...

机构

57 篇 chitkara univers...
51 篇 lovely professio...
49 篇 school of comput...
31 篇 school of comput...
31 篇 centre of interd...
31 篇 xi'an university...
30 篇 school of comput...
30 篇 university of ch...
28 篇 tsinghua univers...
28 篇 school of comput...
27 篇 school of inform...
26 篇 zhejiang univers...
26 篇 microsoft resear...
26 篇 institute of inf...
24 篇 institute of inf...
24 篇 school of comput...
23 篇 school of inform...
21 篇 college of compu...
21 篇 department of co...
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
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
14 篇 zhao hai
13 篇 li yang
13 篇 li li
13 篇 f. ren
13 篇 yang yang
12 篇 li peng
12 篇 wang jing

语言

26,163 篇 英文
855 篇 其他
150 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 27120 条记录，以下是11-20 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：

Dynamic knowledge Graph Construction for Real-Time natural language Understanding in Autonomous Systems

Dynamic Knowledge Graph Construction for Real-Time Natural L...

引用

2025 international conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Subhashini, P. Veeresh, G. Lakhanpal, Sorabh Sharma, Nidhi Saeed, Hazim Y. Soujanya, Rajireddy Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India New Horizon College of Engineering Bangalore India Lovely Professional University Phagwara India Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq Griet Department of Cse Bachupally Telangana Hyderabad India

ISBN: (纸本)9798331527495

This work presents Real- Time Semantic Mapping (RTSM), a new idea for improving the language comprehension in autonomous systems using the knowledge graph. RTSM solely relies on ConceptNet as its dataset and incorporate Spacy to parse and synchronize commends and signals from natural language appropriately. However, through the continuous updating of the knowledge graph, RTSM allows an autonomous system to provide accurate responding to real-time contextual changes with relatively low computational cost. Constant learning and semantic enhancement in this model make RTSM light and easily scalable, putting it at a higher ground as a natural language processing technique for use in autonomous systems. Due to a simple framework that makes it easy to incorporate all the knowledge, the RTSM is ideal in environments that are challenged for time and resources, but need to provide real time interaction capabilities. © 2025 IEEE.

关键词： natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

Evaluation and Analysis of the Chinese Semantic Dependency Understanding Ability of Large language Models 13th

Evaluation and Analysis of the Chinese Semantic Dependency U...

引用

13th international conference on natural language processing and Chinese Computing

作者： Shen, Zizhuo Li, Wei Shao, Yanqiu Beijing Language & Culture Univ Beijing Peoples R China

ISBN: (纸本)9789819794362;9789819794379

Semantic Dependency Graph is a framework for representing deep semantic knowledge through flexible graph structures. While recent works indicate that large language models (LLMs) have impressive language and knowledge understanding abilities, it remains unclear whether they can understand this deep semantic knowledge. To explore this problem, we design four prompt-style probing tasks from aspects of semantic structure and semantic relations to adapt the inherent abilities of LLMs. To ensure thorough evaluation, we conduct extensive experiments in both in-context learning (ICL) and supervised fine-tuning (SFT) scenarios. Our findings indicate that the understanding of deep semantic knowledge requires larger parameter scale, especially the understanding of high-order semantic structure knowledge and semantic relation knowledge. Furthermore, our experiments reveal that while LLMs perform well on the in-domain (ID) test set via SFT, their generalization ability on out-of-domain (OOD) test set remains inadequate.

关键词： Semantic Dependency Graph Large language Model Evaluation

来源：评论

学校读者我要写书评

暂无评论

Evaluating Class Membership Relations in knowledge Graphs Using Large language Models

Evaluating Class Membership Relations in Knowledge Graphs Us...

引用

21st international conference on The Semantic Web (ESWC)

作者： Allen, Bradley P. Groth, Paul T. Univ Amsterdam Amsterdam Netherlands

ISBN: (纸本)9783031789519;9783031789526

A backbone of knowledge graphs are their class membership relations, which assign entities to a given class. As part of the knowledge engineering process, we propose a new method for evaluating the quality of these relations by processing descriptions of a given entity and class using a zero-shot chain-of-thought classifier that uses a natural language intensional definition of a class. We evaluate the method using two publicly available knowledge graphs, Wikidata and CaLiGraph, and 7 large language models. Using the gpt-4-0125-preview large language model, the method's classification performance achieves a macro-averaged F1-score of 0.830 on data from Wikidata and 0.893 on data from CaLiGraph. Moreover, a manual analysis of the classification errors shows that 40.9% of errors were due to the knowledge graphs, with 16.0% due to missing relations and 24.9% due to incorrectly asserted relations. These results show how large language models can assist knowledge engineers in the process of knowledge graph refinement. The code and data are available on Github (https://***/bradleypallen/evaluating-kg-class-memberships-using-llms).

关键词： knowledge engineering large language models knowledge graph refinement natural language generation

来源：评论

学校读者我要写书评

暂无评论

Research on large language model news recommendation technology based on mixed prompt 24

Research on large language model news recommendation technol...

引用

3rd international conference on Artificial Intelligence and Intelligent Information processing, AIIIP 2024

作者： Wei, Han Qu, Dan Xu, Minchen Peng, Sisi Guo, Zhigang Chen, Gang Information Engineering University Henan Zhengzhou China

ISBN: (纸本)9798400707308

With the emergence of massive news every day leading to information overload, it is difficult for people to select the content they are really interested in from numerous news articles. The language knowledge and strong generalization ability of the large model itself provide the possibility of intelligent news recommendation. This paper does not fine-tune the big language model, but uses the prompt engineering technology of the big language model to design a hybrid prompt engineering instruction combining prompt words, context learning and thought chain prompt to stimulate the recommendation performance of the big language model, and realizes the intelligent news recommendation of the big language model. © 2024 Copyright held by the owner/author(s).

关键词： engineering education

来源：评论

学校读者我要写书评

暂无评论

knowledge-Enhanced Utterance Domain Classification with Keywords-Assisted Concept Denoising Network 13th

Knowledge-Enhanced Utterance Domain Classification with Keyw...

引用

13th international conference on natural language processing and Chinese Computing

作者： Huang, Peijie Huang, Boxi Xu, Yuhong Chen, Weiting Li, Jia South China Agr Univ Coll Math & Informat Guangzhou Peoples R China

ISBN: (纸本)9789819794393;9789819794409

Utterance Domain Classification (UDC) is essential for Spoken language Understanding (SLU), a task analogous to short text classification. Short texts are often challenging to understand due to their lack of context, necessitating the enrichment of their semantic representation with supplementary information such as concepts from external knowledge bases. However, the inclusion of concepts introduces noise, making the selection of valuable concepts challenging. This paper proposes a UDC method employing keyword-guided signals to enhance the purity of external knowledge. We use two keyword extraction strategies to construct two types of keywords. A keyword-assisted concept denoising module addresses the concept noise problem, and a knowledge injection module is designed to better integrate concepts into the model. Experimental results on two Chinese SLU datasets demonstrate that our model achieves state-of-the-art performance.

关键词： Utterance domain classification Spoken language understanding knowledge-enhanced methods

来源：评论

学校读者我要写书评

暂无评论

ParaFusion-Extended: Large Scale Paraphrase Dataset Integrating Lexico-Phrasal knowledge 30th

ParaFusion-Extended: Large Scale Paraphrase Dataset Integrat...

引用

30th international conference on Computational and Experimental engineering and Sciences

作者： Jayawardena, Lasal Yapa, Prasan Robert Gordon Univ Garthdee HouseGarthdee Rd Aberdeen AB10 7AQ Scotland Inst Informat Technol 57 Ramakrishna Rd Colombo 00600 Sri Lanka

ISBN: (纸本)9783031774911;9783031774898;9783031774881

Paraphrasing, the art of rephrasing text while retaining its original meaning, lies at the core of natural language understanding and generation. With the rise of demand for more domain-specialized models, high-quality data is more valued than ever;this includes paraphrasing. ParaFusion-Extend (PFE) is a large-scale dataset driven by Large language Models incorporating lexical and phrasal knowledge. The dataset is curated to contain high-quality diverse paraphrase pairs and also separate knowledge bases that could be used for research work and data augmentation models. We show that PFE offers around at least a 30% increase in syntactic and lexical diversity compared to the original data sources that are commonly used. We demonstrate the effectiveness of PFE on several downstream tasks such as few-shot learning and training on sentence embeddings. We utilize a gold-standard evaluation scheme, which is further strengthened by human evaluation that shows the potential of PFE in advancing paraphrase generation.

关键词： paraphrase generation natural language processing natural language generation knowledge representation large language models data augmentation sentence embeddings few-shot learning

来源：评论

学校读者我要写书评

暂无评论

RAVL: A Retrieval-Augmented Visual language Model Framework for knowledge-Based Visual Question Answering 13th

RAVL: A Retrieval-Augmented Visual Language Model Framework ...

引用

13th international conference on natural language processing and Chinese Computing

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

ISBN: (纸本)9789819794362;9789819794379

knowledge-based visual question answering (VQA) requires external knowledge in addition to the image content to answer questions. Recent studies convert images to text descriptions and then generate answers or acquire implicit knowledge using a large language model (LLM). These methods achieve encouraging results with the strong knowledge retrieval and reasoning capabilities of LLMs. However, methods that incorporate LLMs are limited by the discrepancies between images and their text descriptions presented to LLMs. To address this challenge, we present RAVL, a retrieval-augmented visual language model (VLM) framework for knowledge-based VQA. Specifically, we first fine-tune a VLM on the knowledge-based VQA task with inputs consisting of retrieved knowledge and image-question pairs to adapt the VLM to inputs with retrieved knowledge. After that, we adapt the retrieval module to the fine-tuned VLM using supervision signals provided by the VLM, enabling the retrieved knowledge to improve the VLM perplexity. RAVL overcomes the limitation of visual information loss and improves the effectiveness of VLMs with external knowledge. We conduct experiments on OK-VQA dataset and our method achieves 65.73% accuracy, surpassing the previous state-of-the-art method (+3.63%).

关键词： knowledge-based visual question answering Retrieval augmentation Visual language model

来源：评论

学校读者我要写书评

暂无评论

ProSide: knowledge Projector and Sideway for Pre-trained language Models 13th

ProSide: Knowledge Projector and Sideway for Pre-trained Lan...

引用

13th international conference on natural language processing and Chinese Computing

作者： He, Chaofan Lu, Gewei Shen, Liping Shanghai Jiao Tong Univ AI Inst Dept Comp Sci & Engn X LANCE LabMoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9789819794331;9789819794348

Research indicates that incorporating external knowledge into pre-trained language models (PLMs) can enhance their performance on knowledge-driven downstream tasks. However, most approaches either require retraining the model or hardly maintain complete information of knowledge graphs (KGs). In this paper, we introduce a simple but effective ProSide module for PLMs, which includes two components: knowledge Projector and knowledge Sideway. knowledge Projector transforms knowledge representation from entity embedding in KG space to semantic space, while knowledge Sideway retains complete KG information in sideway modules through pre-training. Our ProSide can accommodate different frozen language models, without the need for retraining them. Results on three common knowledge-driven tasks demonstrate that our ProSide method enhances model performance and reaches state-of-the-art level. Additionally, we provide further analysis and case studies to illustrate the mechanism from the perspective of representation space. The code and models are publicly available at Github (Code and models are publicly available at https://***/hcffffff/ProSide).

关键词： knowledge Graph Pre-trained language Models Parameter Efficient Fine-tuning Method

来源：评论

学校读者我要写书评

暂无评论

Sparse Mixture of Experts language Models Excel in knowledge Distillation 13th

Sparse Mixture of Experts Language Models Excel in Knowledge...

引用

13th international conference on natural language processing and Chinese Computing

作者： Xu, Haiyang Liu, Haoxiang Gong, Wei Wang, Hai Deng, Xianjun Univ Sci & Technol China Hefei 230026 Anhui Peoples R China Alibaba Grp Hangzhou Peoples R China Huazhong Univ Sci & Technol Sch Cyber Sci & Engn Wuhan Peoples R China Southeast Univ Sch Comp Sci & Engn Nanjing Peoples R China

ISBN: (纸本)9789819794362;9789819794379

knowledge distillation is an effective method for reducing the computational overhead of large language models. However, recent optimization efforts in distilling large language models have primarily focused on loss functions and training methodologies, with limited attention given to structural improvements of student models. This is largely due to the challenges posed by cross-architecture distillation and the substantial computational resources required for modifying model structures. To address these issues, we introduce a novel method that integrates a sparse mixture of experts (MoE) architecture with low-rank adaptation (LoRA). This combination not only bolsters the capabilities of the student model but also facilitates knowledge distillation using MoE without the necessity of continued pretraining. Experimental results indicate that our approach enhances the model's capabilities compared to dense model distillation, achieving superior performance across a multitude of tasks. We will release our code at https://***/sprogxhy/***.

关键词： Mixture of experts knowledge distillation language models

来源：评论

学校读者我要写书评

暂无评论

LaiDA: Linguistics-Aware In-Context Learning with Data Augmentation for Metaphor Components Identification 13th

LaiDA: Linguistics-Aware In-Context Learning with Data Augme...

引用

13th international conference on natural language processing and Chinese Computing

作者： Liu, Hongde He, Chenyuan Meng, Feiyang Niu, Changyong Jia, Yuxiang Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou Peoples R China

ISBN: (纸本)9789819794423;9789819794430

Metaphor Components Identification (MCI) contributes to enhancing machine understanding of metaphors, thereby advancing downstream natural language processing tasks. However, the complexity, diversity, and dependency on context and background knowledge pose significant challenges for MCI. Large language models (LLMs) offer new avenues for accurate comprehension of complex natural language texts due to their strong semantic analysis and extensive commonsense knowledge. In this research, a new LLM-based framework is proposed, named Linguistics-aware In-context Learning with Data Augmentation (LaiDA). Specifically, ChatGPT and supervised fine-tuning are utilized to tailor a high-quality dataset. LaiDA incorporates a simile dataset for pre-training. A graph attention network encoder generates linguistically rich feature representations to retrieve similar examples. Subsequently, LLM is fine-tuned with prompts that integrate linguistically similar examples. LaiDA ranked 2nd in Subtask 2 of NLPCC2024 Shared Task 9, demonstrating its effectiveness. Code and data are available at https://***/WXLJZ/LaiDA.

关键词： Metaphor Components Identification Linguistics-aware In-context Learning Data Augmentation Large language Models

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 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：