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Advanced Techniques for Chinese Image Captioning: Investigating Attention Mechanisms Based on Object Detection for Chinese Image Caption Generation 5

Advanced Techniques for Chinese Image Captioning: Investigat...

引用

5th international conference on Machine Learning and Computer Application, ICMLCA 2024

作者： Hua, Yongbin Li, Pei'ang Zeng, Xiangjin Xu, Hong School of Computer Science and Engineering Wuhan Institute of Technology Hubei Wuhan China

ISBN: (纸本)9798331530334

The task of image caption generation aims to automatically produce natural language descriptions that match the content of images, integrating the fields of machine vision and natural language processing, which holds significant theoretical and practical value. Inspired by top-down attention mechanisms, this paper proposes an innovative attention model. Utilizing the output of pretrained object detection networks as prior knowledge for images, the model guides the generation of natural language descriptions. By directly incorporating the results of object detection as attention inputs into the text generation network, the model effectively focuses on key descriptive regions of images, thereby significantly enhancing performance. On public Chinese image captioning datasets, this model demonstrates substantial advantages in metrics such as BLEU-4 and METEOR. © 2024 ieee.

关键词： Object recognition

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A Comprehensive Study on Advancements in Text Mining and natural language processing 2

A Comprehensive Study on Advancements in Text Mining and Nat...

引用

2nd ieee international conference on Emerging Research in Computational Science, ICERCS 2024

作者： Mahire, Sarthak Balaji Saravanan, U.K. Murali, M. Prabakaran, N. Vellore Institute of Technology School of Computer Science and Engineering Vellore632014 India Knowledge Institute of Technology Department of Computer Science and Engineering Salem India Sona College of Technology Department of Information Technology Salem India

ISBN: (纸本)9798331534950

Text mining and natural language processing (NLP) have witnessed significant advancements in recent years, driven by the increasing availability of unstructured data and the development of sophisticated machine learning models. This review explores the evolution of text mining and NLP, highlighting the transition from rule-based systems to modern deep learning approaches. This paper reviews various techniques and its impact across various domains. Challenges such as data bias and model interpretability still remain. The paper also discusses future directions, emphasizing the need for fair, interpretable and sustainable NLP systems. This paper aims to provide insights into the current state of text mining and NLP, the challenges faced and potential pathways for future research. © 2024 ieee.

关键词： Adversarial machine learning

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A Multi-Agent Collaborative Framework for Constructing knowledge Graphs from Text 15

A Multi-Agent Collaborative Framework for Constructing Knowl...

引用

15th ieee international conference on knowledge Graph, ICKG 2024

作者： Chen, Gui Liu, Xianhui Tongji University College of Electronic and Information Engineering Shanghai China

ISBN: (纸本)9798331508821

Recent advancements in large language models (LLMs) have significantly improved natural language understanding and generation, making them valuable tools for knowledge graph construction. However, a single LLM often struggles with the complexity of this task, leading to suboptimal results. To address this challenge, we propose a robust multi-agent collaborative framework for constructing knowledge graphs from text. This framework leverages dynamic interactions among specialized agents, including knowledge graph experts, knowledge extraction experts, data processing experts, and domain-specific experts, to effectively build accurate knowledge graphs from text. Additionally, we introduce a novel prompt construction method tailored for knowledge extraction and a revision mechanism to revise preliminary knowledge graphs. These innovations address common issues in knowledge extraction and enhance the quality of model-generated content. Experimental results on four datasets across two tasks (Named Entity Recognition and Relation Extraction) demonstrate that our approach achieves superior performance in the F1 score compared to baseline methods, highlighting its effectiveness and robustness. © 2024 ieee.

关键词： knowledge graph

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Pre-training language Model for Mongolian with Agglutinative Linguistic knowledge Injection

Pre-training Language Model for Mongolian with Agglutinative...

引用

international Joint conference on Neural Networks (IJCNN)

作者： Na, Muhan Liu, Rui Bao, Feilong Gao, Guanglai Inner Mongolia Univ Coll Comp Sci Hohhot Peoples R China Natl & Local Joint Engn Res Ctr Intelligent Infor Hohhot Peoples R China Inner Mongolia Key Lab Mongolian Informat Proc Te Hohhot Peoples R China

ISBN: (纸本)9798350359329;9798350359312

BERT based Pre-training language Model (PLM) has become a crucial step in achieving the best results in various natural language processing (NLP) tasks. However, the current progress, which mainly focuses on major languages such as English and Chinese, has not thoroughly investigated the low-resource languages, particularly agglutinative languages like Mongolian, due to the scarcity of large-scale data resources and the difficulty of understanding agglutinative knowledge. In this paper, we propose a novel PLM for the Mongolian language, that incorporates a novel three-stage agglutinative knowledge injection strategy. Specifically, early-stage injection aims to convert the Mongolia word sequence to the fine-grained sub-word token that comprises a stem and some suffixes;Middle-stage injection designed a morphological knowledge-based masking strategy to enhance the model's ability to learn agglutinative knowledge;Late-stage injection not only involves the model restoring the masked tokens but also predicting the order of suffixes. To address the issue of data scarcity, we create a large-scale Mongolian PLM dataset and three datasets for three downstream tasks, that are News Classification, Name Entity Recognition (NER), and Part-of-Speech (POS) prediction, etc. The experimental results on three downstream tasks demonstrate that our method surpasses the traditional BERT approach and successfully learns agglutinative language knowledge in Mongolian.

关键词： Pre-training language model,Mongolian Agglutinative Linguistic knowledge

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Optimization of natural language processing Models for Multilingual Legal Document Analysis 3

Optimization of Natural Language Processing Models for Multi...

引用

3rd ieee international conference on Intelligent Techniques in Control, Optimization and Signal processing, INCOS 2024

作者： Radhika, A. Bhasin, Narinder Kumar Sabareesh, R. Raju, Yallapragada Ravi Satyanarayana, K.N.V. Raj, I Infant Srk Institute of Technology Department of Cse Vijayawada India Amity University Zonal Head Indian Institute of Banking and Finance Delhi India Coimbatore India Madanapalle Institute of Technology & Science Department of Computer Science &technology Andhra Pradesh Madanapalle India S.R.K.R. Engineering College Department of Ece A.P India K.Ramakrishnan College of Engineering Department of Computer Science & Engineering Tamil Nadu Trichy India

ISBN: (纸本)9798350361186

Multilingual legal document analysis poses unique challenges in the field of natural language processing (NLP) due to the intricacies of legal language and the diverse linguistic landscape of legal texts across jurisdictions. This paper presents an optimization framework designed to enhance the performance of NLP models specifically tailored for multilingual legal document analysis. The proposed framework incorporates advanced techniques in pre-processing, feature engineering, and model architecture to address the complexities inherent in legal language. Leveraging multilingual embeddings and domain-specific knowledge, the model demonstrates improved accuracy in tasks such as named entity recognition, sentiment analysis, and document categorization across a range of languages. Additionally, the optimization framework emphasizes the importance of domain adaptation, acknowledging the nuances and variations in legal terminology across different legal systems. Through a combination of transfer learning and fine-tuning strategies, the model adapts to specific legal domains, ensuring robust performance in diverse legal contexts. Experimental results on a comprehensive dataset of multilingual legal documents validate the effectiveness of the proposed optimization framework. Comparative analyses with baseline models showcase significant improvements in precision, recall, and overall model performance. The findings underscore the potential of the optimized NLP model for applications in legal information retrieval, contract analysis, and legal knowledge management in a multilingual context. This research contributes to the growing body of knowledge in NLP and legal informatics, offering a valuable resource for researchers, practitioners, and developers working on multilingual legal document analysis. The optimized model presented in this paper has the potential to enhance the efficiency and accuracy of automated systems in handling legal texts across diverse linguistic enviro

关键词： knowledge management

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PersianRAG: A Retrieval-Augmented Generation System For Persian language 15

PersianRAG: A Retrieval-Augmented Generation System For Pers...

引用

15th international conference on Information and knowledge Technology, IKT 2024

作者： Hosseini, Hossein Zare, Mohammad Sobhan Mohammadi, Amir Hossein Kazemi, Arefeh Zojaji, Zahra Nematbakhsh, Mohammad Ali Department of Software Engineering University of Isfahan Isfahan Iran ADAPT Research Centre Dublin City University Dublin Ireland Department of Software Engineering Faculty of Computer Engineering University of Isfahan Isfahan Iran

ISBN: (纸本)9798331522254

Retrieval augmented generation (RAG) models, which integrate large-scale pre-trained generative models with external retrieval mechanisms, have shown significant success in various natural language processing (NLP) tasks. However, applying RAG models in Persian language as a low-resource language, poses distinct challenges. These challenges primarily involve the preprocessing, embedding, retrieval, prompt construction, language modeling, and response evaluation of the system. In this paper, we address the challenges towards implementing a real-world RAG system for Persian language called PersianRAG. We propose novel solutions to overcome these obstacles and evaluate our approach using several Persian benchmark datasets. Our experimental results demonstrate the capability of the PersianRAG framework to enhance question answering task in Persian. © 2024 ieee.

关键词： Modeling languages

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Automated Notes and Question Generation 4

Automated Notes and Question Generation

引用

4th ieee international conference on Software engineering and Artificial Intelligence (SEAI)

作者： Zanwar, Siddhi Srikanth, Shusrith Ghei, Anushka Krishnan, Dhriti Rajesh Srinivas, K. S. PES Univ Dept CSE AIML Bengaluru India PES Univ Dept CSE Bengaluru India

ISBN: (纸本)9798350374353;9798350374346

This research paper focuses on augmenting the process of learning a concept through the automated generation of notes and MCQs from educational videos. Our system works in three steps: (i) It transcribes and analyzes the video coupled with extracting keywords and concepts, (ii) then it generates detailed and informative notes, personalized to the video content and (iii) it creates Multiple Choice Questions (MCQs) concerning the content. The system currently uses Machine Learning algorithms and natural language processing to carry out the above-mentioned. This research holds relevance for online courses, flipped classrooms, and self-directed learning along with promoting better understanding of a concept among learners.

关键词： Education Learning Machine learning natural language processing

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SS08: Development and application of knowledge graphs and large language models technology 15

SS08: Development and application of knowledge graphs and la...

引用

15th ieee international conference on knowledge Graph, ICKG 2024

作者： Yang, Yuting Wang, Minghao Hu, Jiexin Yin, Tao Gao, Chao Li, Xiaohe China Institute of Marine Technology and Economy Beijing China Hunan University College of Mechanical and Vehicle Engineering Changsha China

ISBN: (纸本)9798331508821

In the digital age, data science is driving innovation and progress in all walks of life. The opportunities and challenges brought by the explosive growth of data have prompted data scientists to seek innovative solutions - knowledge graphs and large language models, two methods to extract valuable information from structured data and natural language text respectively. This paper summarizes the basic principles,development history and application fields of knowledge graphs and large language models technology, and analyzes the limitations and shortcomings of both;By combining knowledge graphs, large language models is expected to solve many problems such as lack of factual knowledge, illusion and explainability. And in turn, large language models can help knowledge graphs with strong text and language understanding, And if we can bring the two together we might get even more versatile artificial intelligence. © 2024 ieee.

关键词： knowledge graph

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Learning from Failures: Translation of natural language Requirements into Linear Temporal Logic with Large language Models 24

Learning from Failures: Translation of Natural Language Requ...

引用

24th ieee international conference on Software Quality, Reliability and Security (QRS)

作者： Xu, Yilongfei Feng, Jincao Miao, Weikai East China Normal Univ Shanghai Peoples R China

ISBN: (纸本)9798350365634

Formalization of intended requirements is indispensable when using formal methods in software development. However, translating natural language (NL) requirements into formal specifications, such as Linear Temporal Logic (LTL), is error-prone. Although Large language Models (LLMs) offer the potential for automatically translating unstructured NL requirements to LTL formulas, general-purpose LLMs face two major problems: First, low accuracy in translation. Second, high cost of model training and tuning. To tackle these challenges, we propose a new approach that combines dynamic prompt generation with human-computer interaction to leverage LLM for an accurate and efficient translation of unstructured NL requirements to LTL formulas. Our approach consists of two techniques: 1) Dynamic Prompt Generation, which automatically generates the most appropriate prompts for translating the inquired NL requirements. 2) Interactive Prompt Evolution, which helps LLMs to learn from previous translation errors, i.e., erroneous formalizations are amended by users and added as new prompt fragments. Our approach achieves remarkable performance in publicly available datasets from two distinct domains, comprising 36 and 255,000 NL-LTL pairs, respectively. Without human interaction, our method achieves up to 94.4% accuracy. When our approach is extended to another domain, the accuracy improves from an initial 27% to 78% under interactive prompt evolution.

关键词： Requirements engineering Formal Specification Large language Models Prompt engineering natural language processing

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Retrieval Augmented Generation (RAG) based Restaurant Chatbot with AI Testability 10

Retrieval Augmented Generation (RAG) based Restaurant Chatbo...

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ieee 10th international conference on Big Data Computing Service and Machine Learning Applications (ieee BigDataService)

作者： Bhat, Vani Cheerla, Sree Divya Mathew, Jinu Rose Pathak, Nupur Liu, Guannan Gao, Jerry San Jose State Univ Dept Appl Data Sci San Jose CA 95192 USA San Jose State Univ Dept Comp Engn San Jose CA 95192 USA

ISBN: (纸本)9798350366396;9798350366389

Post-COVID the restaurant industry is experiencing a surge in demand, presenting a unique challenge of efficiently managing increased customer flow while ensuring seamless interactions. Chatbots have emerged as an innovative solution to meet the demand increase. The paper addresses the enhancement of AI chatbots through the integration of Retrieval-Augmented Generation (RAG) with the Large language Model (LLM). This paper focuses on the development of a restaurant chatbot that not only engages in natural-language conversations but also addresses context optimization and LLM optimization for restaurant context learning. The approach uses a Neo4j knowledge graph built using the restaurant data as an external source of knowledge. The graph is traversed to match the user question with appropriate answer tokens using Term Frequency - Inverse Document Frequency (TF-IDF) embeddings. The relevant tokens along with user questions are used to provide additional context to the T5 language model to provide nuanced responses to the users. This improvement is quantitatively evidenced by a Bilingual Evaluation Understudy (BLEU) score of 0.60, indicating a high level of precision in language understanding and generation. An extensive evaluation of the chatbot includes assessing AI testability on the level of words, sentences, and information. These evaluations include simulated dialogue assessments and performance analyses, with a focus on the chatbot's ability to retrieve and integrate information. Based on the AI testability evaluation, the models consistently produce more knowledgeable, diverse, and relevant answers as compared with state-of-the-art models with an average information score in the range of 0.6-0.8.

关键词： natural language processing (NLP) Retrieval Augmented Generation (RAG) Large language Model (LLM) Information Retrieval (IR) knowledge Graph AI Testability

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