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共 27256 条记录，以下是211-220 订阅

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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

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Efficient Deployment Using CI/CD: Text Summarization Using NLP in Cloud 1

Efficient Deployment Using CI/CD: Text Summarization Using N...

引用

1st international conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Shingade, Bhuvan Hariharan, Siddharth Patole, Amol Singh, Kishan TPCT's Terna Engineering College Department of Computer Engineering Mumbai India Watumull Institute of Electronics Engineering and Computer Technology Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350355611

The core task in natural language processing (NLP) is text summarization, which condenses important information from large volumes of text into brief summaries. This study reviews text summarization strategies using NLP principles, focusing on two main methods: extractive and abstractive summarization. Abstractive techniques generate summaries by interpreting and rephrasing information, while extractive methods highlight key sentences or paragraphs from the original text. Our study examines various algorithms and models within both paradigms, leading to the development of a novel summarization model. Inspired by advancements in deep learning and transformer architectures, our model integrates state-of-the-art techniques to produce high-quality summaries. We discuss the architecture, training, and evaluation of our model, demonstrating its effectiveness on benchmark datasets. Lastly, we explore potential applications and future directions, emphasizing the importance of custom-built solutions for modern information processing tasks. © 2025 IEEE.

关键词： Inverse problems

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Mongolian-Chinese Cross-Lingual Topic Detection Based on knowledge Distillation and Contrastive Learning Methods 21st

Mongolian-Chinese Cross-Lingual Topic Detection Based on Kn...

引用

21st Pacific Rim international conference on Artificial Intelligence, PRICAI 2024

作者： Wang, Yanli Ji, Yatu Sun, Baolei Wu, Nier Ren, Qing-Dao-Er-Ji Wang, Bailun School of Information Engineering Inner Mongolia University of Technology Hohhot China

ISBN: (纸本)9789819601189

Most topic detection research focuses on cross-language information processing in languages with abundant resources, such as English-Chinese, English-German, and others. There are also some studies targeting low-resource languages such as Vietnamese-Chinese and Tibetan-Chinese cross-language topic detection research. However, there is very little research on Mongolian-Chinese cross-language studies. The primary reasons are the lack of corpora in the Mongolian language and the limitations of conventional topic detection methods in text representation, clustering, and topic representation within the Mongolian language domain. Therefore, this paper proposes a Mongolian-Chinese cross-lingual topic detection method based on knowledge distillation and contrastive learning. Firstly, the pre-trained language model is fine-tuned through knowledge distillation tasks to enable the model to integrate the semantic representation of Mongolian-Chinese cross-lingual texts. Then, the effect of cross-lingual topic detection is improved by combining contrastive learning training between news of different topics. Experimental results show that the proposed method combining knowledge distillation and contrastive learning outperforms other baseline models, achieving at least 2, 0.8, and 3% points higher in F1 score, topic diversity, and topic consistency, respectively. This approach effectively enhances the accuracy of Mongolian-Chinese cross-lingual topic detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Semantic Data Augmentation for Few-Shot Biomedical Named Entity Recognition

Semantic Data Augmentation for Few-Shot Biomedical Named Ent...

引用

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

作者： Zhang, Ying Wang, Weihua College of Computer Science Inner Mongolia University Hohhot China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology Hohhot China

ISBN: (纸本)9798350368741

Biomedical Named Entity Recognition (BioNER) aims to identify and classify entities in biomedical text. This task struggles with data scarcity due to limited annotated data. Although data augmentation is effective, existing methods fail to handle the complex semantic mappings on biomedical terminology, which results in the generated samples lacking semantic diversity. In this paper, we propose a data augmentation method to create semantically diverse and coherent training samples for few-shot BioNER. The method utilizes the Unified Medical language System (UMLS) for entity replacement and combines with a Masked language Model (MLM) to generate contextually relevant words. Experimental results show that our method improves BioNER performance in few-shot scenarios. Compared to all baseline models, our method achieves an average F1 score improvement of 7.6% and 11.6% on the NCBI and JNLPBA datasets, respectively. Our source code and data are available at https://***/ABC8184/SDA. © 2025 IEEE.

关键词： BioNER data augmentation few-shot learning masked language model UMLS

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Medical Prescription Analysis Using Machine Learning 2

Medical Prescription Analysis Using Machine Learning

引用

2nd international conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Rani, S. Jansi Saranya, N. Raghul, T.M. Siva Prasanth, B. Tharun, K. Sri Ramakrishna Engineering College Department of Information Technology Coimbatore India

ISBN: (纸本)9798331505745

Precise reading of medical prescriptions is a critical aspect of healthcare administration, directly relating to patient safety and the effectiveness of treatment. Yet, doctors' poor handwriting frequently results in prescription misreading, placing patients at risk of drug errors. To overcome such an issue, this research envisions an automated prescription analysis system based on Optical Character Recognition (OCR), natural language processing (NLP), and Machine Learning (ML) methodologies. The system is configured to precisely transcribe handwritten prescriptions into structured, readable format, improving access for both pharmacists and patients. The solution proposed in this research study is systematic, encompassing image pre-processing, word segmentation, and smart text recognition, with high precision in reading prescription details. Through incorporating such advanced technologies, the system improves prescription handling efficiency, minimizes medication errors, improves compliance tracking, and personalizes treatment suggestions. Furthermore, its adaptability and scalability allow healthcare organizations to effectively deal with huge prescription volumes, handling varied patient needs. This completely independent application can transform prescription handling, making health care more efficient and improving patient safety. © 2025 IEEE.

关键词： natural language processing systems

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A natural language processing (NLP) Evaluation Framework and its Application in Analyzing Online Public Opinion of Nursing Care Food Market 24

A Natural Language Processing (NLP) Evaluation Framework and...

引用

8th international conference on Computing and Data Analysis, ICCDA 2024

作者： Chen, Zhengyang Fang, Pan Wang, Hongwu Chen, Huiling Public Health Science and Engineering College Tianjin University of Traditional Chinese Medicine Tianjin Tianjin China School of Public Health Nanjing Medical University Jiangsu Nanjing China

ISBN: (纸本)9798400710445

The paper presents a comprehensive natural language processing (NLP) evaluation framework for online public opinion analysis, addressing the limitations of singular NLP techniques. It highlights the significance of public opinion analysis in the era of big data and reviews commonly employed NLP methods. The proposed framework offers universality, comprehensiveness, and flexibility, aiding managers and users in better understanding product sentiments and purchasing behaviors for informed decision-making. The proposed NLP evaluation framework includes five stages, Data Collection, Qualitative Analysis, Sentiment Analysis, Semantic Relationship Mining, and Comprehensive Analysis. It involves techniques like Sentiment Analysis, Topic Modeling, and Co-Occurrence Networks to analyze sentiment and identify semantic relationships. Our case study on nursing care food market validates the framework’s feasibility, proposing enhancements in taste, gifting nature, pricing, and product strategy. Future research will expand its applicability across domains and refine inter-stage connections while maintaining flexibility. © 2024 Copyright held by the owner/author(s).

关键词： natural language processing

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Enhancing Construction Design Efficiency: An Approach to Data Extraction with natural language processing for Technical Drawing

Enhancing Construction Design Efficiency: An Approach to Dat...

引用

international conference of Sustainable Development and Smart Built Environments, SDSBE 2024

作者： Salimi, Ghazal Rahimian, Farzad Okonta, Ebere Donatus Oliver, Stephen Di Stefano, Alessandro Vakaj, Edlira Lane, Nick Teesside University Middlesbrough United Kingdom Birmingham City University Birmingham United Kingdom TaperedPlus Ltd Stockton-on-Tees United Kingdom

ISBN: (纸本)9789819640508

Within the construction and design engineering sphere, integrating advanced technologies has become indispensable for streamlining processes and enhancing productivity. This paper explores the development and implementation of a design assist tool, combining natural language processing (NLP) methodologies to extract semantic information from digital sources. Moreover, different tools’ ability to extract data from email bodies and attachments has been studied. The extraction process, comprising steps such as file detachment with different formats, data labelling, pre-processing techniques such as tokenisation, and feature engineering, requires selecting appropriate techniques, which this paper examines. In this study, data labelling with six features has been done for 278 emails, each of which contained attachments in various formats such as JPEG, PDF, PNG, DWG, etc. Using various tools and patterns such as Regular Expression (Regex), Tokenisation, Stemming, etc., for pre-processing step, made the data ready for training the model. The paper delves into the utility of libraries and models like SpaCy, NLTK, and BERT for efficient data extraction and natural language processing tasks, offering insights into their comparative strengths and suitability for diverse textual analysis needs. The findings suggest that judicious selection of techniques in an NLP project can significantly streamline processes, resulting in time and resource efficiencies. Moreover, the results indicate automated data extraction substantially reduces internal design review time, translating to expedited design turnover and significant cost savings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Drawing (graphics)

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language Models Can See Better: Visual Contrastive Decoding For LLM Multimodal Reasoning

Language Models Can See Better: Visual Contrastive Decoding ...

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

作者： Pang, Yuqi Yang, Bowen Tu, Haoqin Cao, Yun Zhang, Zeyu Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China University of California Santa CruzCA United States

ISBN: (纸本)9798350368741

Although Large language Models (LLMs) excel in reasoning and generation for language tasks, they are not specifically designed for multimodal challenges. Training Multimodal Large language Models (MLLMs), however, is resource-intensive and constrained by various training limitations. In this paper, we propose the Modular-based Visual Contrastive Decoding (MVCD) framework to move this obstacle. Our framework leverages LLMs' In-Context Learning (ICL) capability and the proposed visual contrastive-example decoding (CED), specifically tailored for this framework, without requiring any additional training. By converting visual signals into text and focusing on contrastive output distributions during decoding, we can highlight the new information introduced by contextual examples, explore their connections, and avoid over-reliance on prior encoded knowledge. MVCD enhances LLMs' visual perception to make it see and reason over the input visuals. To demonstrate MVCD's effectiveness, we conduct experiments with four LLMs across five question answering datasets. Our results not only show consistent improvement in model accuracy but well explain the effective components inside our decoding strategy. Our code will be available at https://***/Pbhgit/MVCD. © 2025 IEEE.

关键词： contrastive-example decoding In-Context Learning output distributions

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PresiUniv at FinCausal 2025 Shared Task: Applying Fine-tuned language Models to Explain Financial Cause and Effect with Zero-shot Learning 9

PresiUniv at FinCausal 2025 Shared Task: Applying Fine-tuned...

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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： Jeenoor, Medha Aziz, Madiha Vaidyanathan, Saipriya Dipika Samantraya, Avijit Mathias, Sandeep Department of Computer Science and Engineering Presidency University Bangalore India

ISBN: (纸本)9798891762091

Transformer-based multilingual question-answering models are used to detect causality in financial text data. This study employs BERT (Devlin et al., 2019) for English text and XLM-RoBERTa (Conneau et al., 2020) for Spanish data, which were fine-tuned on the SQuAD datasets (Rajpurkar et al., 2016) (Rajpurkar et al., 2018). These pre-trained models are used to extract answers to the targeted questions. We design a system using these pre-trained models to answer questions, based on the given context. The results validate the effectiveness of the systems in understanding nuanced financial language and offers a tool for multi-lingual text analysis. Our system is able to achieve SAS scores of 0.75 in Spanish and 0.82 in English. © 2025 Association for Computational Linguistics.

关键词： Zero-shot learning

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Empowering smart traffic avoidance using natural language processing 3

Empowering smart traffic avoidance using Natural language pr...

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3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Kalaiselvi, V.K.G. Vishnu Chandar, K.R. Vishwa, G. Tirunagari, Sharanya Hariharan, Shanmugasundaram Krishnamoorthy, Murugaperumal Sri Sairam Engineering College Department of Information Technology Tamilnadu India Vardhaman College of Engineering Department of Artificial Intelligence and Data Science Hyderabad India Vardhaman College of Engineering Department of Electrical and Electronics Engineering Hyderabad India

ISBN: (纸本)9798331515911

The integration of natural language processing (NLP) into smart traffic systems offers a novel approach to enhancing real-time traffic management and optimization. This paper explores how NLP can be leveraged to interpret and analyze vast amounts of unstructured traffic-related data, such as social media updates, traffic reports, and incident logs. A key component of this approach is the use of a BERT (Bidirectional Encoder Representations from Transformers) classifier, which enables more accurate extraction and classification of relevant traffic information from textual data. By processing this data in real time, the BERT-based NLP system can provide more precise traffic predictions, identify congestion patterns, and optimize traffic flow dynamically. The study highlights the potential of NLP, particularly through the use of BERT, in improving the efficiency and responsiveness of traffic management systems, ultimately leading to reduced travel times, lower emissions, and enhanced urban mobility. The app also includes user-friendly interfaces that allow drivers and city planners to access real-time updates, personalized route recommendations, and predictive analytics. The system's adaptability ensures that it can respond quickly to unexpected incidents, such as accidents or road closures, further enhancing urban mobility. The study highlights the transformation potential of this app in improving the efficiency and responsiveness of traffic management systems, ultimately leading to reduced travel times, lower emissions, and a significant improvement in overall urban transportation experiences. © 2025 IEEE.

关键词： Efficiency

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