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

14,702 篇 会议
666 篇 期刊文献
101 册 图书
37 篇 学位论文

馆藏范围

15,505 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

11,112 篇 工学
- 10,444 篇 计算机科学与技术...
- 5,444 篇 软件工程
- 1,501 篇 信息与通信工程
- 983 篇 电气工程
- 945 篇 控制科学与工程
- 448 篇 生物工程
- 245 篇 网络空间安全
- 223 篇 化学工程与技术
- 197 篇 机械工程
- 177 篇 生物医学工程（可授...
- 150 篇 电子科学与技术（可...
- 122 篇 安全科学与工程
- 117 篇 交通运输工程
2,492 篇 理学
- 1,158 篇 数学
- 662 篇 物理学
- 518 篇 生物学
- 400 篇 统计学（可授理学、...
- 244 篇 化学
- 240 篇 系统科学
2,429 篇 管理学
- 1,748 篇 图书情报与档案管...
- 779 篇 管理科学与工程(可...
- 236 篇 工商管理
- 119 篇 公共管理
1,832 篇 文学
- 1,776 篇 外国语言文学
- 169 篇 中国语言文学
535 篇 医学
- 310 篇 临床医学
- 285 篇 基础医学(可授医学...
- 125 篇 公共卫生与预防医...
281 篇 法学
- 251 篇 社会学
242 篇 教育学
- 229 篇 教育学
100 篇 农学
93 篇 经济学
10 篇 艺术学
7 篇 哲学
4 篇 军事学

主题

3,639 篇 natural language...
1,790 篇 natural language...
952 篇 computational li...
754 篇 semantics
700 篇 machine learning
634 篇 deep learning
521 篇 natural language...
378 篇 accuracy
375 篇 computational mo...
356 篇 training
354 篇 large language m...
342 篇 sentiment analys...
329 篇 feature extracti...
311 篇 data mining
290 篇 speech processin...
264 篇 transformers
258 篇 speech recogniti...
238 篇 neural networks
219 篇 support vector m...
217 篇 iterative method...

机构

85 篇 carnegie mellon ...
52 篇 university of ch...
45 篇 tsinghua univers...
44 篇 carnegie mellon ...
42 篇 zhejiang univers...
41 篇 national univers...
35 篇 univ chinese aca...
35 篇 nanyang technolo...
35 篇 carnegie mellon ...
34 篇 university of sc...
34 篇 university of wa...
33 篇 alibaba grp peop...
32 篇 gaoling school o...
32 篇 stanford univers...
30 篇 tsinghua univ de...
30 篇 school of artifi...
28 篇 peking universit...
27 篇 harbin institute...
26 篇 univ sci & techn...
26 篇 microsoft resear...

作者

55 篇 zhou guodong
50 篇 neubig graham
46 篇 liu yang
39 篇 sun maosong
36 篇 zhang min
34 篇 liu qun
33 篇 smith noah a.
28 篇 schütze hinrich
26 篇 wen ji-rong
26 篇 lapata mirella
25 篇 liu zhiyuan
24 篇 chang kai-wei
23 篇 zhou jie
23 篇 yang diyi
23 篇 zhao hai
23 篇 zhao wayne xin
21 篇 chua tat-seng
20 篇 dredze mark
18 篇 biemann chris
18 篇 fung pascale

语言

13,014 篇 英文
2,377 篇 其他
131 篇 中文
18 篇 法文
14 篇 土耳其文
2 篇 德文
2 篇 西班牙文
2 篇 俄文

检索条件"任意字段=Conference on empirical methods in natural language processing"

共 15506 条记录，以下是1391-1400 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：

A Dual-Perspective Metaphor Detection Framework Using Large language Models

A Dual-Perspective Metaphor Detection Framework Using Large ...

引用

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

作者： Lin, Yujie Liu, Jingyao Gao, Yan Wang, Ante Su, Jinsong School of Informatics Xiamen University Xiamen China

ISBN: (纸本)9798350368741

Metaphor detection, a critical task in natural language processing, involves identifying whether a particular word in a sentence is used metaphorically. Traditional approaches often rely on supervised learning models that implicitly encode semantic relationships based on metaphor theories. However, these methods often suffer from a lack of transparency in their decision-making processes, which undermines the reliability of their predictions. Recent research indicates that LLMs (large language models) exhibit significant potential in metaphor detection. Nevertheless, their reasoning capabilities are constrained by predefined knowledge graphs. To overcome these limitations, we propose DMD, a novel dual-perspective framework that harnesses both implicit and explicit applications of metaphor theories to guide LLMs in metaphor detection and adopts a self-judgment mechanism to validate the responses from the aforementioned forms of guidance. In comparison to previous methods, our framework offers more transparent reasoning processes and delivers more reliable predictions. Experimental results prove the effectiveness of DMD, demonstrating state-of-the-art performance across widely-used datasets. © 2025 IEEE.

关键词： dual-perspective large language models metaphor detection metaphor theories self-judgment

来源：评论

学校读者我要写书评

暂无评论

Automatic Analysis of Substantiation in Scientific Peer Reviews

Automatic Analysis of Substantiation in Scientific Peer Revi...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Guo, Yanzhu Shang, Guokan Rennard, Virgile Vazirgiannis, Michalis Clavel, Chloe Inst Polytech Paris Ecole Polytech LIX Palaiseau France Inst Polytech Paris Telecom Paris LTCI Palaiseau France INRIA Paris France Linagora Paris France

ISBN: (纸本)9798891760615

With the increasing amount of problematic peer reviews in top AI conferences, the community is urgently in need of automatic quality control measures. In this paper, we restrict our attention to substantiation ' one popular quality aspect indicating whether the claims in a review are sufficiently supported by evidence ' and provide a solution automatizing this evaluation process. To achieve this goal, we first formulate the problem as claim-evidence pair extraction in scientific peer reviews, and collect SubstanReview, the first annotated dataset for this task. SubstanReview consists of 550 reviews from NLP conferences annotated by domain experts. On the basis of this dataset, we train an argument mining system to automatically analyze the level of substantiation in peer reviews. We also perform data analysis on the SubstanReview dataset to obtain meaningful insights on peer reviewing quality in NLP conferences over recent years. The dataset is available at https://***/YanzhuGuo/SubstanReview. © 2023 Association for Computational Linguistics.

关键词： natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

Text Data Pre-processing for Time-series Modelling 33

Text Data Pre-Processing for Time-series Modelling

引用

33rd International conference on Radioelektronika (RADIOELEKTRONIKA)

作者： Pomenkova, Jitka Korab, Petr Strba, David Brno Univ Technol Dept Radio Elect Brno Czech Republic Zeppelin Univ Lentiamo Czech Republic Lentiamo Prague Czech Republic

ISBN: (纸本)9798350398342

The paper proposes an algorithm of text data representation for time-series econometric modeling. We link two areas in empirical econometrics, text mining and time-series modeling, and provide a multi-step methodology for processing qualitative text data for quantitative time-series models. We also present an empirical example of text data processing of research article meta-data in economics and show its applicability to sentiment analysis using annual data from 1933 - 2015

关键词： text mining natural language processing time-series modeling data pre-processing

来源：评论

学校读者我要写书评

暂无评论

language-Based Colorization with Sparse Attention and Multi-scale Cross-Modal Semantic Alignment 24th

Language-Based Colorization with Sparse Attention and Mult...

引用

24th International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2024

作者： Zhang, Ying Gao, Yutong Liu, Xuan Zhang, Lefeng Liu, Xianggan Jiang, Shan Ethnic Language Intelligent Analysis and Security Governance Minzu University of China Beijing China Faculty of Data Science City university of Macau China Natural Language Processing and Knowledge Graph Lab Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9789819615476

language-based colorization generates realistic and aesthetically appealing colors by leveraging the guidance of intuitive and user-friendly natural language descriptions. Previous methods in language-based image colorization face several significant challenges, including limited color richness, color bleeding, color distortions, and inconsistent color styles. These issues often arise due to the reliance on dense attention mechanisms, which can lead to an overemphasis on global features at the expense of local details. Furthermore, many feature fusion modules for text and grayscale images are not designed to achieve adequate alignment of the diverse feature representations, resulting in suboptimal colorization outcomes. In this paper, we explore a novel module that employs sparse attention to mitigate the issue of color bleeding and limited colorfulness in image colorization tasks. By introducing a sparse attention mechanism, our method enables more flexible allocation of computational resources with a focus on color awareness. Additionally, we propose a module that effectively aligns grayscale images with color descriptions, thereby improving the consistency and quality of the colorization results. This module leverages the synergy between image features and language descriptions to solve long-standing issues like color bleeding. empirical evaluations demonstrate that our approach surpasses recent state-of-the-art techniques in both automatic and language-based colorization tasks, validating the effectiveness and robustness of our proposed method in generating high-quality, visually appealing colorized images. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

INDUS: Effective and Efficient language Models for Scientific Applications

INDUS: Effective and Efficient Language Models for Scientifi...

引用

2024 conference on empirical methods in natural language processing: Industry Track, EMNLP 2024

作者： Bhattacharjee, Bishwaranjan Trivedi, Aashka Muraoka, Masayasu Ramasubramanian, Muthukumaran Udagawa, Takuma Gurung, Iksha Pantha, Nishan Zhang, Rong Dandala, Bharath Ramachandran, Rahul Maskey, Manil Bugbee, Kaylin Little, Mike Fancher, Elizabeth Gerasimov, Irina Mehrabian, Armin Sanders, Lauren Costes, Sylvain Blanco-Cuaresma, Sergi Lockhart, Kelly Allen, Thomas Grezes, Felix Ansdell, Megan Accomazzi, Alberto El-Kurdi, Yousef Wertheimer, Davis Pfitzmann, Birgit Ramis, Cesar Berrospi Dolfi, Michele de Lima, Rafael Teixeira Vagenas, Panagiotis Mukkavilli, S. Karthik Staar, Peter Vahidinia, Sanaz McGranaghan, Ryan Lee, Tsendgar IBM Research AI United States NASA MFSC United States UAH United States Navteca United States NASA GSFC United States NASA Ames United States Harvard-Smithsonian CfA United States NASA HQ United States JPL Smart City ERZ Zurich Switzerland

ISBN: (纸本)9798891761667

Large language models (LLMs) trained on general domain corpora showed remarkable results on natural language processing (NLP) tasks. However, previous research demonstrated LLMs trained using domain-focused corpora perform better on specialized tasks. Inspired by this insight, we developed INDUS, a comprehensive suite of LLMs tailored for the closely-related domains of Earth science, biology, physics, heliophysics, planetary sciences and astrophysics, and trained using curated scientific corpora drawn from diverse data sources. The suite of models include: (1) an encoder model trained using domain-specific vocabulary and corpora to address NLP tasks, (2) a contrastive-learning based text embedding model trained using a diverse set of datasets to address information retrieval tasks and (3) smaller versions of these models created using knowledge distillation for applications which have latency or resource constraints. We also created three new scientific benchmark datasets, CLIMATE-CHANGE NER (entity-recognition), NASA-QA (extractive QA) and NASA-IR (IR) to accelerate research in these multi-disciplinary fields. We show that our models outperform both general-purpose (RoBERTa) and domain-specific (SCIBERT) encoders on these new tasks as well as existing tasks in the domains of interest. Furthermore, we demonstrate the use of these models in two industrial settings- as a retrieval model for large-scale vector search applications and in automatic content tagging systems. © 2024 Association for Computational Linguistics.

关键词： NASA

来源：评论

学校读者我要写书评

暂无评论

Attack Prompt Generation for Red Teaming and Defending Large language Models

Attack Prompt Generation for Red Teaming and Defending Large...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Deng, Boyi Wang, Wenjie Feng, Fuli Deng, Yang Wang, Qifan He, Xiangnan Univ Sci & Technol China Hefei Peoples R China Natl Univ Singapore Singapore Singapore Meta AI Menlo Pk CA USA

ISBN: (纸本)9798891760615

Large language models (LLMs) are susceptible to red teaming attacks, which can induce LLMs to generate harmful content. Previous research constructs attack prompts via manual or automatic methods, which have their own limitations on construction cost and quality. To address these issues, we propose an integrated approach that combines manual and automatic methods to economically generate high-quality attack prompts. Specifically, considering the impressive capabilities of newly emerged LLMs, we propose an attack framework to instruct LLMs to mimic human-generated prompts through in-context learning. Furthermore, we propose a defense framework that fine-tunes victim LLMs through iterative interactions with the attack framework to enhance their safety against red teaming attacks. Extensive experiments on different LLMs validate the effectiveness of our proposed attack and defense frameworks. Additionally, we release a series of attack prompts datasets named SAP with varying sizes, facilitating the safety evaluation and enhancement of more LLMs. Our code and dataset is available on https://***/Aatrox103/SAP.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

SRAP-Agent: Simulating and Optimizing Scarce Resource Allocation Policy with LLM-based Agent

SRAP-Agent: Simulating and Optimizing Scarce Resource Alloca...

引用

2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Ji, Jiarui Li, Yang Liu, Hongtao Du, Zhicheng Wei, Zhewei Shen, Weiran Qi, Qi Lin, Yankai Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

ISBN: (纸本)9798891761681

Public scarce resource allocation plays a crucial role in economics as it directly influences the efficiency and equity in society. Traditional studies including theoretical model-based, empirical study-based and simulation-based methods encounter limitations due to the idealized assumption of complete information and individual rationality, as well as constraints posed by limited available data. In this work, we propose an innovative framework, SRAP-Agent (Simulating and Optimizing Scarce Resource Allocation Policy with LLM-based Agent), which integrates Large language Models (LLMs) into economic simulations, aiming to bridge the gap between theoretical models and real-world dynamics. Using public housing allocation scenarios as a case study, we conduct extensive policy simulation experiments to verify the feasibility and effectiveness of the SRAP-Agent and employ the Policy Optimization Algorithm with certain optimization objectives. The source code can be found in https://***/jijiarui-cather/SRAPAgent_Framework. © 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

AUDIO-FREE PROMPT TUNING FOR language-AUDIO MODELS 49

AUDIO-FREE PROMPT TUNING FOR LANGUAGE-AUDIO MODELS

引用

49th IEEE International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Li, Yiming Wang, Xiangdong Liu, Hong Chinese Acad Sci Inst Comp Technol Beijing Key Lab Mobile Comp & Pervas Device Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Contrastive language-Audio Pretraining (CLAP) is pre-trained to associate audio features with human language, making it a natural zero-shot classifier to recognize unseen sound categories. To adapt CLAP to downstream tasks, prior works inevitably require labeled domain audios, which limits their scalability under data scarcity and deprives them of the capability to detect novel classes as the original CLAP. In this work, by leveraging the modality alignment in CLAP, we propose an efficient audio-free prompt tuning scheme aimed at optimizing a few prompt tokens from texts instead of audios, which regularizes the model space to avoid overfitting the seen classes as well. Based on this, a multi-grained prompt design is further explored to fuse global and local information. Experiments on several tasks demonstrate that our approach can boost the CLAP and outperform other training methods on model performance and training efficiency. While conducting zero-shot inference on unseen categories, it still shows better transferability than the vanilla CLAP. Moreover, our method is flexible enough even if only knowing the downstream class names. The code is available at https://***/Ming-er/Audio-Free-P-Tuning.

关键词： prompt tuning with texts language-audio models multi-grained prompt design training efficiency

来源：评论

学校读者我要写书评

暂无评论

V-DPO: Mitigating Hallucination in Large Vision language Models via Vision-Guided Direct Preference Optimization

V-DPO: Mitigating Hallucination in Large Vision Language Mod...

引用

2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Xie, Yuxi Li, Guanzhen Xu, Xiao Kan, Min-Yen National University of Singapore Singapore

ISBN: (纸本)9798891761681

Large vision-language models (LVLMs) suffer from hallucination, resulting in misalignment between the output textual response and the input visual content. Recent research indicates that the over-reliance on the Large language Model (LLM) backbone, as one cause of the LVLM hallucination, inherently introduces bias from language priors, leading to insufficient context attention to the visual inputs. We tackle this issue of hallucination by mitigating such over-reliance through preference learning. We propose Vision-guided Direct Preference Optimization (V-DPO) to enhance visual context learning at training time. To interpret the effectiveness and generalizability of V-DPO on different types of training data, we construct a synthetic dataset containing both response- and image-contrast preference pairs, compared against existing human-annotated hallucination samples. Our approach achieves significant improvements compared with baseline methods across various hallucination benchmarks. Our analysis indicates that V-DPO excels in learning from image-contrast preference data, demonstrating its superior ability to elicit and understand nuances of visual context. Our code is publicly available at https://***/YuxiXie/V-DPO. © 2024 Association for Computational Linguistics.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Efficient fine-tuning of short text classification based on large language model 24

Efficient fine-tuning of short text classification based on ...

引用

International conference on Modeling, natural language processing and Machine Learning (CMNM)

作者： Wang, Likun Yunnan Univ Sch Informat Sci & Engn Kunming 650500 Yunnan Peoples R China

ISBN: (纸本)9798400709760

With the booming development of social networks, a massive amount of short texts emerge every day, containing valuable information such as user interests and intentions. Therefore, the mining and classification of short text information is particularly important. However, the inherent characteristics of sparse features and high noise in short texts limit the performance of traditional machine learning methods in short text classification. Meanwhile, many neural network models often rely on a large amount of annotated data during the training process, but obtaining sufficient annotated data is a challenging task in practical situations. Taking inspiration from recent large-scale language models, this article proposes an efficient fine-tuning method for short text classification based on the LLaMA large-scale language model. Utilizing the powerful learning ability of large language models to expand text information, fine-tuning the freezing model and instruction learning through LoRA can more fully classify downstream specific tasks. From the experimental results obtained from real datasets, it can be seen that the method proposed in this paper has achieved an improvement in the accuracy of short text classification.

关键词： short text classification large language model LLaMA LoRA finetuning

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 << < 136 137 138 139 140 141 142 143 144 145 > >>

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：