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检索条件"任意字段=International Conference on Natural Language Processing and Knowledge Engineering"

共 26995 条记录，以下是71-80 订阅

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An Automated Grading System for Competition Theses Assisted by NLP Models 24

An Automated Grading System for Competition Theses Assisted ...

引用

8th international conference on Electronic Information Technology and Computer engineering, EITCE 2024

作者： Xue, Sijuan Duan, Xu Yang, Manqi College of Information Engineering Hainan Vocational University of Science and Technology Hainan Haikou China

ISBN: (纸本)9798400710094

As the digital transformation of education continues to advance, the inefficiency and subjectivity of traditional manual scoring methods have become increasingly prominent. To address this issue, this study developed an automated scoring process. The process includes several key steps: reading text files, training latent Dirichlet allocation (LDA) topic models, matching texts with conditional sentence libraries, and performing natural language processing (NLP) analysis. The scoring method used in this study follows the standards proposed by advocates of text scoring conditions to comprehensively evaluate the completeness and accuracy of the text. The entire paper is comprehensively evaluated from four dimensions: completeness, content, summary quality, and writing ability. © 2024 Copyright held by the owner/author(s).

关键词： natural language processing systems

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Semi-Supervised knowledge Distillation Framework towards Lightweight Large language Model for Spoken language Translation

Semi-Supervised Knowledge Distillation Framework towards Lig...

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

作者： Rajkhowa, Tonmoy Chowdhury, Amartya Roy Tripathi, Achyut Mani Sharma, Sanjeev Pandey, Om Jee Varanasi India Indian Institute of Technology Dharwad Karnataka India

ISBN: (纸本)9798350368741

Even though large language models (LLMs) have demonstrated remarkable performance across various natural language processing tasks, their application in speech-related tasks has largely remained underexplored. This work addresses this gap by incorporating acoustic features into an LLM which can be fine-tuned for downstream direct speech-to-text translation and automatic speech recognition tasks. To address the computational demands associated with fine-tuning LLMs, a novel self and semi-supervised knowledge distillation technique is proposed to implement a lightweight LLM having 50% lesser parameters. Validated on the MuST-C and Librispeech datasets, this technique achieves over 92% of the performance of the larger LLM, demonstrating both robust performance and computational efficiency. © 2025 IEEE.

关键词： Direct Speech-to-Text Translation knowledge Distillation Large language Model

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A formal corpus of mathematical theorems: a bridge from natural language to first-order logic 24

A formal corpus of mathematical theorems: a bridge from natu...

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13th international conference on Computing and Pattern Recognition, ICCPR 2024

作者： Li, Lulu Li, Zhengtao Hu, Peng Zhang, Hao School of Electronic and Communication Engineering Tianjin Normal University Tianjin China

ISBN: (纸本)9798400717482

Currently, there is a significant gap in the conversion of mathematical theorems from natural language to logical expressions, specifically in the form of first-order predicate logic. To address this issue, this paper presents a corpus of mathematical theorems expressed in first-order predicate logic and explores its necessity and practical applications. The primary objective of this work is to introduce a new paradigm for the logical transformation of mathematical theorems by detailing specific the construction process. The paper aims to demonstrate the potential functions and roles of this corpus, providing solid data support for automated proofs, formal verification, and other related fields. This corpus facilitates the automated processing and understanding of mathematical theorems. The contribution of this paper lies in proposing an innovative solution to bridge the gap between natural language processing and formal logic research. This study aims to enhance the performance and accuracy of algorithms and tools for processing mathematical theorems by constructing a corpus of theorems in first-order predicate logic. © 2024 Copyright held by the owner/author(s).

关键词： Mathematical transformations

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Temporal knowledge Graph Link Prediction Using Synergized Large language Models and Temporal knowledge Graphs 5th

Temporal Knowledge Graph Link Prediction Using Synergized La...

引用

5th international conference on Neural Computing for Advanced Applications (NCAA)

作者： Chen, Yao Shen, Yuming Guangdong Univ Foreign Studies Sch Informat Sci & Technol Guangzhou Peoples R China

ISBN: (纸本)9789819770069;9789819770076

Although large language models and temporal knowledge graphs each have significant advantages in the field of artificial intelligence, they also face certain challenges. However, through collaboration, large language models and temporal knowledge graphs can complement each other, addressing their respective shortcomings. This collaborative approach aims to harness the potential feasibility and practical effectiveness of large language models as external knowledge bases for temporal knowledge graph reasoning tasks. In our research, we have meticulously designed a synergized model that leverages the knowledge from the graph as prompts. The answers generated by the large language model undergo careful processing before being seamlessly incorporated into the training dataset. The ultimate goal is to significantly enhance the reasoning capabilities of temporal knowledge graphs. Experimental results underscore the positive impact of this synergized model on the completion tasks of temporal knowledge graphs, showcasing its potential to address gaps in knowledge and improve overall performance. While its influence on prediction tasks is relatively weak, the collaborative synergy demonstrates promising avenues for further exploration and development in the realm of AI research.

关键词： Large language models Temporal knowledge graphs Synergetic pattern Completion task Prediction task

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MAG: Improved Multi-Intent Detection and Slot Filling Model Based on Mamba 24

MAG: Improved Multi-Intent Detection and Slot Filling Model ...

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8th international conference on Electronic Information Technology and Computer engineering, EITCE 2024

作者： Gong, Tianxi Feng, Baiming College of Computer Science and Engineering Northwest Normal University Gansu Lanzhou China

ISBN: (纸本)9798400710094

Intention detection and slot filling are two main tasks in the field of natural language understanding in natural language processing. Since the two tasks are highly correlated, the two tasks are often modeled jointly. Because of the high information density of natural spoken language, there may be multiple intentions in one utterance. The joint model of multi-intention detection and slot filling has become the most important task in natural language understanding. Because of the presence of multiple intents, there are often multiple centers of gravity in all sentences, and the task tests the performance of the model more. After learning and understanding Mamba model, the author was attracted by its clever structure and advanced performance. Mamba model has the characteristics of selective input and can adjust model parameters according to the input data, which means that Mamba has unique advantages in the task of intention detection. Taking this as inspiration, the author applied Mamba model to multi-intention detection and slot filling tasks, which improved the quality of semantic feature extraction, improved the accuracy of the model, accelerated the running speed of the model and reduced the space occupation of the model. © 2024 Copyright held by the owner/author(s).

关键词： Semantics

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Assistance for Visually Impaired individuals using Artificial Intelligence and natural language processing

Assistance for Visually Impaired individuals using Artificia...

引用

2025 IEEE international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Sathvig, Sri K. Bharathi.ln, Priyanka Santhosh.m Ramya, J. St. Joseph's College of Engineering Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331529833

People with Visual Impairments are vulnerable to various challenges in their everyday lives ranging from Access to Information, Travelling to places, Recognizing their surroundings, Isolated Lifestyle and much more. The Proposed solution aims to address these socially inclusive problems through the use of Artificial Intelligence, natural language processing, Machine Learning and Computer Vision. The feature index involves using Object Detection models like Yolo v5 for Obstacle avoidance, Integration of Google Map API for Seamless Navigation and NLP based Optical Character Recognition (OCR) for Text Recognition. An average accuracy of 86 percentage was achieved across all models. This solution aims to provide Better Lifestyle, Increased accessibility and Reduced Dependability for the Community of Visually Impaired Individuals. © 2025 IEEE.

关键词： Artificial Intelligence Computer Vision Machine Learning natural language processing

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AI-Powered Transcript Summarization and Question Answering for YouTube and Wikipedia 2

AI-Powered Transcript Summarization and Question Answering f...

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2nd international conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Sharma, Kanishka Srivastava, Sarthak Pilania, Harsh Maurya, Pranjal Kumar Prakash, Ayushi CSE Department AKGEC Engineering College Ghaziabad India AKGEC Engineering College Ghaziabad India

ISBN: (纸本)9798331530389

With the rapid growth of online content, there is an increasing demand for efficient tools that facilitate content understanding and knowledge retention. This research focuses on the development of an AI-powered system designed to automate summarization, question answering, and quiz generation from YouTube transcripts and Wikipedia articles. The system employs advanced natural language processing (NLP) techniques to process and distill complex information into clear and concise summaries. The process begins with the extraction of text through the YouTube Transcript API or web scraping. The Pegasus-XSum model is then used for summarizing the content, ensuring that key information is retained while minimizing text length. For question answering, the Llama model is employed to generate contextually relevant answers based on the provided content. Additionally, the system generates quizzes by extracting entities from the text to enhance interactive learning. This technology aims to improve content comprehension, making it especially valuable for educational settings and knowledge management. By integrating summarization, question answering, and quiz generation into a single platform, the system supports efficient learning and better retention of critical information. This research demonstrates the potential of NLP in addressing the challenges of information overload and facilitating effective knowledge acquisition. © 2025 IEEE.

关键词： knowledge acquisition

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Attention-Based Image Caption Generation 5th

Attention-Based Image Caption Generation

引用

5th international conference on Data Science, Machine Learning and Applications

作者： Manasa, M. Sowmya, D. Reddy, Y. Supriya Sreedevi, Pogula G Pulla Reddy Engn Coll Dept Comp Sci & Engn Data Sci Kurnool 5185007 India G Pulla Reddy Engn Coll Dept Comp Sci & Engn Kurnool 5185007 India Rajeev Gandhi Mem Coll Engn & Technol Dept Comp Sci & Engn Nandyal 518501 India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

The automatic development of meaningful, detailed textual descriptions for supplied images is a difficult task in the fields of computer vision and natural language processing. As a result, an AI-powered image caption generator can be incredibly useful for producing captions. In this study, we present a unique method for creating picture captions utilizing an attention mechanism that concentrates on pertinent areas of the image while it creates captions. On benchmark datasets, our model, which uses deep neural networks to extract picture attributes and produce captions, obtains state-of-the-art results, confirming the effectiveness of the attention mechanism in raising the caliber of the generated captions. We also offer a thorough evaluation of the performance of our approach and talk about potential future directions for enhancing image caption generation.

关键词： Image Caption Computer Vision natural language processing Attention Mechanism Performance

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Mixed Multimodal Contrastive Learning for Enhancing Detection and Correction of Faked and Misspelled Chinese Characters 13th

Mixed Multimodal Contrastive Learning for Enhancing Detectio...

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13th international conference on natural language processing and Chinese Computing

作者： He, Rui Wang, Zhongqing Wang, Hongling Soochow Univ Nat Language Proc Lab Suzhou Peoples R China

ISBN: (纸本)9789819794423;9789819794430

Visual Chinese Character Checking (C3) aims to detect and correct errors in handwritten Chinese text images, including faked characters and misspelled characters. This task is beneficial for subsequent tasks by improving the efficiency of identifying errors in handwritten text. Recent methods are mainly based on Optical Character Recognition (OCR) and Pre-trained language Models (PLMs). Visual Chinese Character Checking is an emerging task, and relevant research has made progress. However, we believe that existing work has not fully leveraged the inherent knowledge of pre-trained models and has not addressed the semantic bias issue between pre-trained models and the character checking task. These challenges result in deficiencies in recognizing misspelled Chinese characters and correcting misused characters. Therefore, we propose various multimodal contrastive learning methods based on image-to-image and image-to-text comparisons. These methods are used throughout the processes of character recognition, error detection, and correction. By aligning the semantic feature representations among different models, our approach makes these models more suitable for the Visual Chinese Character Checking task, thereby enhancing their capabilities.

关键词： Chinese Character Checking Chinese Spell Checking Contrastive Learning Optical Character Recognition

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Transforming Model-to-Model Interactions with Advanced natural language processing: A Deep Learning Approach 1

Transforming Model-to-Model Interactions with Advanced Natur...

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

作者： Keerthana, M. Kusuma, C.D. Banakar, Lakshmi Shivabasappa Mallika, K. Dakshina Kannada India

ISBN: (纸本)9798350355611

The following discusses a feasibility review being carried out for the use of natural language processing in the context of capturing Model-to-Model transformations in visual models, in particular, the extraction of information from text labels in BPMN and UML notations. Three significant tasks that the paper aims at taking care of include the extraction of verb/noun relationships, parsing conjunctive/disjunctive statements, and distinguishing abbreviations and acronyms. The current state of the art of NLP tools - such as CoreNLP, Stanford Stanza, Flair, Spacy, AllenNLP, BERT, and ELECTRA - coupled with some custom models, such as BERT-BiLSTM-CRF and ELMo, has resulted in these benchmarks. This system uses a BiLSTM-CRF tagging model trained on augmented data to do well on classes where the other models are confused. The model's performance is good in extracting noun and verb phrases irrespective of the recognition of words present in the shorttext labels, which are used in most of the UML and BPMN models. This study also raises challenges in the detection of abbreviations and acronyms and in processing conjunctive and disjunctive statements. Experimental results note that although Stanza-based implementation excels in the extraction of phrases and conjunctive phrase processing, the BERT-BiLSTM-CRF model is outdone in the detection of verb phrases. This goes a long way to show that, as promising as deep learning techniques are, they still bear their flaws but can be improved upon for application in different domains in NLP. © 2025 IEEE.

关键词： Deep Learning Information Extraction Model-to-Model Transformations natural language processing

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