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

1,581 篇 会议
28 册 图书
23 篇 期刊文献

馆藏范围

1,632 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,110 篇 工学
- 1,018 篇 计算机科学与技术...
- 750 篇 软件工程
- 245 篇 信息与通信工程
- 90 篇 电气工程
- 69 篇 控制科学与工程
- 67 篇 生物工程
- 42 篇 化学工程与技术
- 24 篇 仪器科学与技术
- 24 篇 安全科学与工程
- 23 篇 生物医学工程（可授...
- 21 篇 交通运输工程
- 19 篇 机械工程
- 17 篇 网络空间安全
- 16 篇 电子科学与技术（可...
- 15 篇 建筑学
360 篇 管理学
- 288 篇 图书情报与档案管...
- 107 篇 管理科学与工程(可...
- 49 篇 工商管理
343 篇 理学
- 131 篇 数学
- 124 篇 物理学
- 75 篇 生物学
- 55 篇 统计学（可授理学、...
- 48 篇 系统科学
- 41 篇 化学
41 篇 教育学
- 41 篇 教育学
38 篇 法学
- 32 篇 社会学
37 篇 文学
- 31 篇 外国语言文学
30 篇 医学
- 27 篇 临床医学
- 22 篇 基础医学(可授医学...
19 篇 农学
- 17 篇 作物学
14 篇 经济学
4 篇 军事学

主题

183 篇 natural language...
65 篇 natural language...
55 篇 semantics
50 篇 web services
46 篇 sentiment analys...
42 篇 computational mo...
42 篇 accuracy
41 篇 deep learning
40 篇 information retr...
37 篇 data mining
36 篇 classification (...
35 篇 machine learning
35 篇 artificial intel...
33 篇 ontology
31 篇 speech recogniti...
31 篇 training
29 篇 data models
28 篇 semantic web
26 篇 search engines
26 篇 feature extracti...

机构

7 篇 spoken language ...
7 篇 sri lanka instit...
7 篇 faculty of compu...
6 篇 faculty of infor...
4 篇 key laboratory o...
4 篇 fujian yirong in...
4 篇 korea univ nat l...
4 篇 school of inform...
4 篇 international in...
4 篇 information engi...
3 篇 big data and art...
3 篇 graduate school ...
3 篇 national univers...
3 篇 institute of art...
3 篇 carnegie mellon ...
3 篇 school of inform...
3 篇 chitkara univers...
3 篇 beijing univ pos...
3 篇 ipswich
3 篇 department of in...

作者

7 篇 nakagawa seiichi
5 篇 ren fuji
5 篇 zhao tiejun
5 篇 rim hae-chang
4 篇 heyan huang
4 篇 shikano kiyohiro
4 篇 fuji ren
4 篇 liu ting
3 篇 li li
3 篇 rykowski jarogni...
3 篇 nakamura satoshi
3 篇 zheng dequan
3 篇 ngoc thanh nguye...
3 篇 medhat walaa
3 篇 kinshuk
3 篇 bogdan trawiński
3 篇 sun xu
3 篇 mizera-pietraszk...
3 篇 bollegala danush...
3 篇 kitsuregawa masa...

语言

1,592 篇 英文
37 篇 其他
11 篇 中文

检索条件"任意字段=6th International Conference on Advanced Language Processing and Web Information Technology"

共 1632 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：

Proxy Tuning for Financial Sentiment Analysis: Overcoming Data Scarcity and Computational Barriers 9

Proxy Tuning for Financial Sentiment Analysis: Overcoming Da...

引用

Joint Workshop of the 9th Financial technology and Natural language processing, FinNLP 2025, the 6th Financial Narrative processing, FNP 2025, and the 1st Workshop on Large language Models for Finance and Legal, LLMFinLegal 2025, co-located with the 31st international conference on Computational Linguistics, COLING 2025

作者： Wang, Yuxiang Wang, Yuchi Liu, Yi Bao, Ruihan Harimoto, Keiko Sun, Xu National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China Mizuho Securities Co. Ltd. Japan

ISBN: (纸本)9798891762091

Financial sentiment analysis plays a pivotal role in the financial domain. However, the task remains challenging due to the nuanced nature of financial sentiment, the need for high interpretability, and the scarcity of high-quality datasets. To address these issues, we leverage recent advancements in large language models (LLMs) and propose to adapt proxy tuning for financial sentiment analysis. Proxy tuning efficiently transfers knowledge from a pre-trained expert model to a controllable base model by incorporating logit differences, steering the base model toward the desired sentiment representation. Our method offers significant advantages: (1) it is training-free, reducing computational demands and data dependency;(2) it achieves promising performance, with a 36.67% improvement over the base model and over 90% of the tuned model’s performance;and (3) it is highly adaptable, functioning in a plug-and-play manner without requiring access to model architectures or weights. these results demonstrate the potential of proxy tuning as an efficient and practical solution for financial sentiment analysis in data-scarce scenarios. © 2025 Association for Computational Linguistics.

关键词： Finance

来源：评论

学校读者我要写书评

暂无评论

web Development and Evaluation of Instructional Objectives’ Quality Using Natural language processing 6th

Web Development and Evaluation of Instructional Objectives’...

引用

6th international conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Mohanta, Jyotiprava Jain, Mahim Jain, Mahim M. Mandal, Shyamal Kumar Das Advance Technology Development Centre Indian Institute of Technology Kharagpur India Aerospace Engineering Indian Institute of Technology Kharagpur India

ISBN: (纸本)9789819780891

Outcome-based education is currently recommended in every field of education by the accreditation bodies. Outcome-based education is the process of developing the curriculum in a well-defined framework. the stated outcome or the instructional objective should be properly defined for successful implementation. Here, a methodology is proposed where the curriculum designer can check whether the instructional objective is properly designed. the methodology consists of website development and a machine learning model (ML) using natural language processing. the input to the model is an instructional objective statement, and the result will be in three categories: poor, good, and perfect. the website consists of a Flask-written backend and a Next. js-developed frontend. the backend was integrated with the ML model. For the model, the Bidirectional Encoder Representations from Transformers model is used. the designed model was applied to 50 instructional objective datasets for training the model and 40 datasets for testing the prediction. the BERT model was found to be effective. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Enriching RDF Data with LLM Based Named Entity Recognition and Linking on Embedded Natural language Annotations 6th

Enriching RDF Data with LLM Based Named Entity Recognition ...

引用

6th international conference on Knowledge Graphs and Semantic web, KGSWC 2024

作者： Freund, Michael Dorsch, Rene Schmid, Sebastian Wehr, thomas Harth, Andreas Fraunhofer Institute for Integrated Circuits IIS Nürnberg Germany Friedrich-Alexander-Universität Erlangen-Nürnberg Nürnberg Germany

ISBN: (纸本)9783031812200

In this paper, we present a processing pipeline for transforming natural language annotations in RDF graphs into machine-readable and interoperable semantic annotations. the pipeline uses Named Entity Recognition (NER) and Entity Linking (EL) techniques based on a foundational Large language Model (LLM), combined with a Knowledge Graph (KG) based knowledge injection approach for entity disambiguation and self-verification. through a running example in the paper, we demonstrate that the pipeline can increase the number of semantic annotations in an RDF graph derived from information contained in natural language annotations. the evaluation of the proposed pipeline shows that the LLM-based NER approach produces results comparable to those of fine-tuned NER models. Furthermore, we show that the pipeline using a chain-of-thought prompting style with factual information retrieved via link traversal from an external KG achieves better entity disambiguation and linking than both a pipeline without chain-of-thought prompting and an approach relying only on information within the LLM. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Insights on Improving Reasoning and Increasing Capabilities in Large language Models 6th

Insights on Improving Reasoning and Increasing Capabilities ...

引用

6th international conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Krisshna, S. Varun Sendhil, R. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9789819780891

Large language models (LLMs) based on the Transformer architecture are designed to understand and generate human-like text by learning patterns and relationships from vast amounts of textual data. these models have been reported to have "sparks" of artificial general intelligence owing to their very attractive properties such as high generalization and performance in unseen data. these models have demonstrated strong capabilities in generalization and performance on novel data, leading some to speculate they may contain early sparks of artificial general intelligence. However, true AGI remains an unachieved goal requiring breakthroughs beyond current techniques. LLMs have poor mathematical reasoning capabilities, inherit biases present in training data, and can hallucinate and deliver false information. In this chapter, we discuss several methods that improve the current state-of-the-art models through the use of external tools such as web browsing, better prompting techniques, scaling, using Reinforcement learning (RL) and tree-based search algorithms. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Enhancing Radiology Report Interpretation with Large-Scale language Models: A Two-Stage Fine-Tuning Approach 27

Enhancing Radiology Report Interpretation with Large-Scale L...

引用

27th international conference on advanced Communications technology, ICACT 2025

作者： Pi, Sun-Woo Park, Myeong-Soo Lee, Byoung-Dai Department of Computer Science Graduate School Kyonggi University Suwon Korea Republic of Division of AI and Computer Engineering Kyonggi University Suwon Korea Republic of

ISBN: (纸本)9791188428137

Radiology reports contain complex medical terminology and specialized knowledge, making them difficult for both patients and medical professionals to interpret. this study aims to address this challenge by developing a large-scale language model specifically designed for interpreting chest radiology reports. We focus on four key natural language processing (NLP) tasks—summarization, paraphrasing, abbreviation interpretation, and question answering—using a synthetic dataset derived from the MIMIC-CXR reports and GPT-3.5 Turbo. To enhance the model’s performance, we propose a two-stage supervised fine-tuning (SFT) process, incorporating real-world medical data from PubMedQA and MedQA, in addition to the synthetic dataset. the resulting models, Model-1 and Model-2, were evaluated based on accuracy, conciseness, and clarity, using test data not seen during training. Experimental results demonstrated that the proposed two-stage SFT method achieved strong performance across all four tasks, providing comparable performance to models such as GPT-3.5, Bard, Llama2, and MedAlpaca in key evaluation metrics, despite using a relatively smaller number of parameters. these findings suggest that synthetic data, when combined with domain-specific datasets, can significantly improve the interpretive capabilities of large-scale language models in the medical domain. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Radiology

来源：评论

学校读者我要写书评

暂无评论

Process Modeler vs. Chatbot: Is Generative AI Taking over Process Modeling?

Process Modeler vs. Chatbot: Is Generative AI Taking over Pr...

引用

international Workshops which were held in conjunction with the 6th international conference on Process Mining, ICPM 2024

作者： Klievtsova, Nataliia Benzin, Janik-Vasily Mangler, Juergen Kampik, Timotheus Rinderle-Ma, Stefanie TUM School of Computation Information and Technology Technical University of Munich Garching Germany SAP Signavio Berlin Germany

ISBN: (纸本)9783031822247

Large language models (LLMs) have become a promising tool for automating complex tasks such as process model generation from text. In order to evaluate the capabilities of LLMs in generating process models, it is crucial to provide means to assess the output quality. A few studies have already provided key performance indicators for assessing aspects such as completeness of the models in a quantitative way. In this paper, we focus on the qualitative assessment of generated process models generated by LLMs based on a user survey. By analyzing user preferences, we aim to determine whether LLM-generated process models meet the needs and expectations of experts. Our analysis reveals that 60% of users, regardless of their modeling experience, prefer LLM-generated models over human-created ground truth models. © the Author(s) 2025.

关键词： Modeling languages

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Different Models for Sarcastic and Non-sarcastic Text Detection Using NLP 6th

Comparative Analysis of Different Models for Sarcastic and N...

引用

6th international conference on Intelligent Communication, Control and Devices, ICICCD 2024

作者： Verma, Deval Kumar, Pranay Agarwal, Manish Verma, Gaurav Sombatteera, Sujanya Butdisuwan, Sujin Bennett University Greater Noida India Jaypee Institute of Information Technology Noida India Shinawatra University Samkhok Thailand

ISBN: (纸本)9789819783281

Sarcasm recognition is another aspect of natural language processing (NLP) used for sentiment analysis. It uses mathematical inference to show and categorize a word or phrase’s polarity for sardonic. In literature, sarcasm detection has been applied to various datasets such as the Reddit corpus and Twitter datasets, which mostly contain noisy language and labels. this work aims to identify sarcasm in the following datasets: news headlines, generic, rhetorical questions, hyperbole, English and Arabic text to overcome these issues. this study compares various models intended to distinguish between sardonic and non-sardonic information to understand this intricate problem efficiently. Moreover, standard datasets are trained using deep learning techniques, including transformers like GPT and BERT. the transformer-based models (GPT and BERT) outperform other models, with F1-scores of 87% and 85%, respectively. the model with the lowest performance is the logistic regression model, having a F1-score of 73%. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Scalable Summarization of Long-Form Video Transcripts using NLP 6

Scalable Summarization of Long-Form Video Transcripts using ...

引用

6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Minu, M.S. Mirajkar, Feroza D. Mohanaprakash, T.A. Kaviya, D. Jasmine Fathima, K. Maha, S. Department of CSE SRM Institute of Science and Technology Ramapuram Chennai India Department of Electronics and Communication Engineering FoET Khaja Bandanawaz University Kalaburagi India Department of CSE SOET CMR University Bengaluru India SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (纸本)9798331522667

this research designs a system that, with the help of NLP, would sum up the YouTube video transcripts properly without losing any important information. Day in day out, time keeps on recording increasing numbers of videos that are housed in internet-based platforms. Educating is the most prominent explanation for all the global content being available and accessible. One might also train on the different networks such as Google, Facebook, Instagram, YouTube, and once again Google. the underlying truth that the viewer has to watch the entire film to really understand and appreciate the broader perspective shows one key element—is comparable with images where so much information and data can be extracted and understood from just one frame;it indicates that data extraction from videos is a both an important and challenging question. thus, the aim of this research project is to comfortably shorten and summarize the length of the video transcript so that it can easily be understood. the proposed approach is to get transcripts by using the link given from the video provided by the user and then use TD-IDF combined with the BART transformer in order to text summarize. the model developed here successfully satisfies the consideration of video URLs shared by the user and the exact summary duration needed. At the end, this results in the detailed summary of the transcript for the videos. Comparing with the other proposed methods put forward, the final text translation has been found to be present much earlier than what the results initially suggested. Moreover, a significant point has to be added that the final text absolutely depicts the theme of the movie without any kind of change or alteration. © 2025 IEEE.

关键词： Inverse Document Frequency User-provided video link Natural language processing Term Frequency Video summarization

来源：评论

学校读者我要写书评

暂无评论

Application of Computer information technology in Intelligent Analysis and Decision-Making Support of Diagnosis and Treatment Data

Application of Computer Information Technology in Intelligen...

引用

6th international conference on Cyber Security Intelligence and Analytics, CSIA 2024

作者： Gao, Yan Zhang, Yinsong Yunnan Agricultural University Yunnan China

ISBN: (纸本)9783031882869

In view of the problem that TCM diagnosis and treatment data are complex, diverse and lack unified standards, this paper introduced computer information technology, combined with NLP and LSTM models, to improve the intelligent analysis ability of TCM diagnosis and treatment data, and provide scientific support for clinicians through an auxiliary decision-making system, thereby improving the efficiency and accuracy of diagnosis and treatment. First, this paper collected and integrated a large amount of medical records, treatment records, prescription information and other data from different TCM diagnosis and treatment platforms and medical institutions, standardizes data in different formats, and used natural language processing (NLP) technology for semantic analysis and data cleaning. then, it built a classification and prediction model based on LSTM (Long Short Time Memory) to realize intelligent diagnosis and treatment recommendation generation for common diseases in view of the complex symptoms and individual differences unique to TCM diagnosis and treatment. Finally, based on the previous analysis and model results, an auxiliary decision system was developed to provide doctors with auxiliary decision suggestions in the treatment of complex symptoms and medication regimen recommendations during the diagnosis and treatment process. the results showed that after the use of the intelligent auxiliary decision system, the diagnosis and treatment time reduction rate was between 21.8% and 23.0%, and the accuracy rate reached 88.0%. Compared with traditional manual analysis methods, the efficiency of TCM diagnosis and treatment data processing has been significantly improved after the application of computer information technology. the introduction of computer information technology provides a new solution for the intelligent analysis and decision-making support of TCM diagnosis and treatment data, effectively solving the problems of complex, heterogeneous and insuffici

关键词： Data reduction

来源：评论

学校读者我要写书评

暂无评论

Analyzing and Visualizing Urdu News Articles Using NLP and Concept Graphs

Analyzing and Visualizing Urdu News Articles Using NLP and C...

引用

international conference on Advancements in Computational Sciences (ICACS)

作者： Aneela Mehmood Malik Tahir Hassan Qadeer Baig School of Systems and Technology University of Management and Technology Lahore Pakistan Faculty of information technology University of Central Punjab Lahore Pakistan Founder of Newsarchives.pk Canada

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

this paper investigates Urdu news articles using the natural language processing (NLP) concept graph generation technique. We gather news data (web scraping), do text pre-processing, translate it into Urdu, and calculate the similarity between articles by performing cosine similarities. At last, we present concept graphs (also referred to as conjunction graphs)-relationships between news articles—to visually tell the tale. Our method gives insights into the structure and connections in an empirical news corpus, pointing out significant trends and patterns.

关键词： Translation Accuracy Scalability Instruments Semantics Market research Natural language processing Human in the loop

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164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：