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共 27256 条记录，以下是301-310 订阅

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VTU Question Classification System based on BERT and Bloom's Taxonomy Ring' 5

VTU Question Classification System based on BERT and Bloom's...

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5th international conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Punyatoya, Swastisudha Vidya, N. Pradhan, Chittaranjan Thejaswini, S. Sahoo, Abhaya Kumar Shaista Afrin, Z. Sandhya, S. Kalinga Institute of Industrial Technology School of Computer Engineering Bhubaneswar India Mvj College of Engineering Department of Cse Bangalore India

ISBN: (纸本)9798331522100

The "VTU Question Classification System Based on BERT and Bloom's Taxonomy"integrates advanced natural language processing (NLP) techniques to automate the classification of questions related to education. Utilizing the BERT (Bidirectional Encoder Representations from Transformers) model, the system aims to categorize questions into respective cognitive levels as defined by Bloom's Taxonomy. This initiative addresses the challenges of manual question classification, which is often time-consuming and prone to errors. The application encompasses stages of data gathering, preprocessing, model fine-tuning, and developing an accessible user interface. The expected outcomes include an efficient NLP system capable of accurately classifying questions, thereby aiding in aligning educational assessments with specific learning objectives. This work contributes to the enhancement of educational assessment techniques and offers potential for future advancements in the application of NLP in education. © 2025 IEEE.

关键词： natural language processing systems

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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

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Automatic Identification of the American Sign language Using Machine Learning 2

Automatic Identification of the American Sign Language Using...

引用

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

作者： Kodali, Ravi Kishore Pattanayak, Arindam National Institute of Technology Warangal Department of Electronics and Communication Engineering Warangal India

ISBN: (纸本)9798331505745

Communication is essential, providing a means to express thoughts, emotions, and ideas. Sign language is a fundamental form of communication for those who are deaf, have hearing impairments, or are non-verbal. This research uses machine learning to develop an effective algorithm for identifying the alphabet in American Sign language (ASL) through natural hand movements. The system combines image processing, machine learning, and artificial intelligence, especially a convolutional neural network (CNN), to translate ASL gestures into accurate and understandable output. After training the model on a dataset that includes sign language images, the results demonstrate the system's ability to identify hand gestures related to the English alphabet accurately. © 2025 IEEE.

关键词： Convolutional neural networks

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2nd international Workshop on Value engineering in Artificial Intelligence, VALE 2024

2nd International Workshop on Value Engineering in Artificia...

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2nd international Workshop on Value engineering in Artificial Intelligence, VALE 2024

ISBN: (纸本)9783031854620

The proceedings contain 16 papers. The special focus in this conference is on Value engineering in Artificial Intelligence. The topics include: Value Learning for Value-Aligned Route Choice Modeling via Inverse Reinforcement Learning;adversarial Behaviour in Moral Value Expression: A Statistical and Frame-Semantic Analysis of Social Media;vision language Models as Values Detectors;leveraging Large language Models to Identify the Values Behind Arguments;value Promotion Scheme Elicitation Using natural language processing: A Model for Value-Based Agent Architecture;neuro-Symbolic Classification of Basic Human Values;value-Based Decision-Making in Software Agents: A Systematic Literature Review;modelling Moral Decision-Making in a Contractualist Artificial Agent;satisfaction in Negotiation by Structured Values and Propositions;towards the Incorporation of Social Values in Automated Negotiation Strategies;context Matters: Contextual Value-Based Deliberation in Water Consumption Scenarios;Opportunities and Challenges of the Use of Forgetting in Symbolic XAI;Values-Aligned, Responsible Sharing (VaRS): A Methodology and a Blueprint;From the Pascal Wager to Value engineering: A Glance at AI Risks and How to Address Them.

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AI-Driven Transcription and Analysis for Medical Conversations 2

AI-Driven Transcription and Analysis for Medical Conversatio...

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

作者： Pisal, Tejas Hajare, Rasika Verma, Prateek Gote, Pradnyawant M. Jiet, Mosses Chaudhri, Shiv Nath Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

This paper presents an advanced system that integrates Google's Speech Recognition API with the Gemini-l.5-flash model to improve audio processing and response generation, particularly in the context of healthcare communication. The system is designed to transcribe audio files of doctor-patient conversations accurately and generate contextually relevant responses using AI. A user-friendly interface, developed with Tkinter, allows seamless uploading and processing of these audio files. The integration of these technologies aims to enhance the efficiency and effectiveness of automated response systems, thereby improving patient-provider communication. The system's capabilities were evaluated using real-world healthcare datasets, demonstrating its potential to streamline the workflow from audio input to meaningful textual output. This work not only showcases the practical application of AI in healthcare but also lays the groundwork for future innovations in automated medical communication systems. © 2025 IEEE.

关键词： natural language processing systems

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Harnessing Dimensional Contrast and Information Compensation for Sentence Embedding Enhancement

Harnessing Dimensional Contrast and Information Compensation...

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

作者： He, Kang Ding, Yuzhe Li, Bobo Wang, Haining Li, Fei Teng, Chong Ji, Donghong Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China

ISBN: (纸本)9798350368741

Unsupervised sentence embedding learning excels through positive sample construction and instance-level contrastive learning (ICL). However, this approach can lead to over-compression and dimensional contamination from noisy data augmentation and unconstrained ICL processes. To mitigate these issues, we propose a novel enhancement method, MSSE, which incorporates an Information Compensation Mechanism (ICM) and a Dimensional-Level Contrastive Learning Mechanism (DCM). ICM, inspired by the information bottleneck principle, prevents excessive compression in representation learning. DCM constrains ICL and minimizes information leakage across dimensions. Experimental results show that MSSE surpasses existing competitive baselines across seven STS tasks, including unsupervised and few-shot scenarios. The source code is available at https://***/Hekang001/MSSE. © 2025 IEEE.

关键词： contrastive learning natural language processing Sentence embedding unsupervised learning

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Contextualizing Entity Representations for Zero-Shot Relation Extraction with Masked language Models 24th

Contextualizing Entity Representations for Zero-Shot Relati...

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24th international conference on knowledge engineering and knowledge Management, EKAW 2024

作者： Capshaw, Riley Blomqvist, Eva Linköping University Linköping581 83 Sweden

ISBN: (纸本)9783031777912

knowledge graphs (KGs) and their related ontologies constitute a key component in modern knowledge-based systems. However, hand-crafting these is not scalable, particularly due to the rate at which knowledge changes in many real-world applications. Partially automating the process of extracting and even modelling knowledge has therefore been a subject of research for many years. Nevertheless, accurate and reliable KG construction from natural language documents still remains a difficult task with many challenges, even in light of the impressive recent advances in language modelling. This paper focuses on one of those challenges, namely the extraction of accurate entity representations from text documents in order to facilitate relation extraction (RE). We present a novel method for generating document-contextualized input representations for entities using a masked language model (MLM) without the need for any sort of fine-tuning. These representations are then used as inputs to the same MLM that generated them, alleviating the need to include entire documents when prompting. Our results show that these representations 1) improve the ability of the MLMs BERT and RoBERTa to identify statements that represent correct relations between two entities;and 2) allow BERT to perform on par with the fine-tuned MLMs BioBERT and PubMedBERT. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： knowledge graph

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IHILLM-RAG: A safe and private medical large language model based on intelligent hardware interaction and retrieval-augmented generation(RAG) 4

IHILLM-RAG: A safe and private medical large language model ...

引用

4th international Computational Imaging conference, CITA 2024

作者： Zhou, Zibo Yang, Yi Ren, Tianling School of Integrated Circuit Tsinghua University Beijing100084 China Tsinghua University Beijing100084 China

ISBN: (纸本)9781510688834

Large language models (LLMs) have shown great potential in medical question answering and health management in the rapidly evolving healthcare landscape these ***, even with domain-specific pretraining, LLMs may potentially hallucinate and produce factually inaccurate outcomes. In addition, in the field of healthcare, due to the different habits, backgrounds, and physical conditions of patients, it is necessary to strengthen user-oriented medical assistants, but there is relatively little exploration of personalized medical assistants based on LLM. In this study, we propose a medical assistant LLM that combines external medical knowledge base and intelligent monitoring hardware interaction, which is called IHILLM-RAG. On the one hand, we build RAG system through external medical knowledge base to improve the security and accuracy of medical assistant and reduce the phenomenon of hallucination. On the other hand, we design the human physiological signal monitoring chip as a"human-machine interface" and carry out physical interaction with the human body and data interaction with LLM, so as to enhance the LLM's learning and memory of the user's own habits and improve the degree of personalization. We evaluated IHILLM-RAG on the important medical question-answering datasets, our experimental results demonstrate that IHILLM-RAG compares to the state-of-the-art open source models, the accuracy of the three datasets was improved by an average of 62.4%, 50.4%and 48.6%. Through this work, we provide a safer and more personalized method for healthcare which achieves a new paradigm of medical consultation with"Private AI-doctor" at any time and anywhere. © 2025 SPIE.

关键词： Digital storage

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Integrated AI-Based Pipeline for Law Enforcement Application: FIR Assistance 5th

Integrated AI-Based Pipeline for Law Enforcement Application...

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5th international conference on Computing and Network Communications, CoCoNet 2023

作者： Amruth, A. Ramanan, R. Paul, Rhea Pranav, R. Gupta, Deepa Vekkot, Susmitha Nair, Priyanka C. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9789819747108

This paper presents an integrated AI-based pipeline for law enforcement applications, specifically targeting the creation of first information report (FIR) in the Kannada language. The pipeline comprises a speech processing block utilizing automatic speech recognition (ASR) models to convert spoken Kannada into text, and a natural language processing block employing advanced AI techniques for information extraction and generating concise FIR summaries. The research focuses on optimizing and streamlining the FIR creation process by leveraging AI technologies and overcoming data scarcity in the Kannada language through data augmentation techniques. OpenAI’s Whisper is used for ASR, resulting in two-word error rate in this use case. For information extraction, a Bi-LSTM neural network is employed for named entity recognition (NER) with a performance of 94%. The proposed NER model utilizes three tags, which can be expanded to accommodate additional tags as per FIR requirements. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： FIR filters

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BiLD: Bi-directional Logits Difference Loss for Large language Model Distillation 31

BiLD: Bi-directional Logits Difference Loss for Large Langua...

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31st international conference on Computational Linguistics, COLING 2025

作者： Li, Minchong Zhou, Feng Song, Xiaohui KTH Royal Institute of Technology Stockholm Sweden OPPO AI Center Beijing China

ISBN: (纸本)9798891761964

In recent years, large language models (LLMs) have shown exceptional capabilities across various natural language processing (NLP) tasks. However, such impressive performance often comes at the cost of an increased parameter size, posing significant challenges for widespread deployment. To address this issue, knowledge distillation (KD) provides a solution by transferring knowledge from a large teacher model to a smaller student model. In this paper, we explore the task-specific distillation of LLMs at the logit level. Our investigation reveals that the logits of fine-tuned LLMs exhibit a more pronounced long-tail distribution compared to those of vision models, with hidden "noise" in the long tail affecting distillation performance. Furthermore, existing logits distillation methods often struggle to effectively utilize the internal ranking information from the logits. To address these, we propose the Bi-directional Logits Difference (BiLD) loss. The BiLD loss filters out the long-tail noise by utilizing top-k teacher and student logits, and leverages the internal logits ranking information by constructing logits differences. To evaluate BiLD loss, we perform comprehensive experiments on 13 datasets with two types of LLMs. Our results show that the BiLD loss, with only the top-8 logits, outperforms supervised fine-tuning (SFT), vanilla KL loss, and five other distillation methods from both NLP and CV fields. Code is available at https://***/fpcsong/BiLD. © 2025 Association for Computational Linguistics.

关键词： Directional logging

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