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检索条件"任意字段=7th International Conference on Advanced Language Processing and Web Information Technology"

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Proceedings - ALPIT 2008, 7th international conference on advanced language processing and web information technology

Proceedings - ALPIT 2008, 7th International Conference on Ad...

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ALPIT 2008, 7th international conference on advanced language processing and web information technology

ISBN: (纸本)9780769532738

the proceedings contain 101 papers. the topics discussed include: exploring various features in semantic role labeling;transformation rule learning without rule templates: a case study in part of speech tagging;word sense disambiguation based on relation structure;Chinese sentence similarity measure based on words and structure information;email classification using semantic feature space;a clustering approach of conceptual sentence groups;deriving a semantic classification tree of Korean verbs based on semantic features;Korean syntactic analysis using dependency rules and segmentation;tree kernel-based semantic relation extraction using unified dynamic relation tree;near-duplicates detection for Vietnamese documents in large database;latent semantic kernels for wordnet: transforming a tree-like structure into a matrix;and a new feature-fusion sentence selecting strategy for query-focused multi-document summarization.

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A Comparative Analysis of Quantized and Non-Quantized BERT Model Performance for the Low-Resource Tagalog language through Binary Text Classification 24

A Comparative Analysis of Quantized and Non-Quantized BERT M...

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7th international conference on Computational Intelligence and Intelligent Systems, CIIS 2024

作者： Tomas, John Paul Q. Ngo, Gabriel Richmond R. Reyes, Aramis Faye D. Santiago, Spencer Ivan S. School of Information Technology Mapua University Makati Philippines

ISBN: (纸本)9798400717437

the development of artificial intelligence and its integration to human society is an unstoppable force. Hence, many subsets of this phenomenon such as natural language processing are expected to continuously evolve alongside other advancements made in the field. One of the challenges under such subset are low-resource languages which impact the growth of artificial intelligence in countries whose language or languages are not globally popular and therefore lack training data and/or tailored NLP technologies for machine and deep learning use. the fairly recent, state-of-the-art transformer-based language model Bidirectional Encoder Representations from Transformers or BERT has given NLP research for low-resource languages such as Filipino/Tagalog the opportunity to advance, but is held back for many by its high hardware requirements. therefore, the researchers wish to contribute to the accessibility of Filipino natural language processing research by determining if quantization would be beneficial for low-resource BERT models such as for Tagalog. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Deep learning

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LLM for Uniform information Extraction Using Multi-task Learning Optimization 7th

LLM for Uniform Information Extraction Using Multi-task Lear...

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8th Asia Pacific web and web-Age information Management Joint international conference on web and Big Data

作者： Li, Ying Tan, Zhen Xiao, Weidong Natl Univ Def Technol Natl Key Lab Informat Syst Engn Kaifu Peoples R China

ISBN: (纸本)9789819600540;9789819600557

With the increasing maturity of deep learning technology, large language models have shown excellent performance in the field of natural language processing, but the performance of information extraction is yet to be further improved. In this paper, we use 7B Llama-2 as a base model training to obtain a large language model capable of using natural language to guide information extraction tasks, which can solve the challenges faced by traditional information extraction methods, and also innovatively use multi-task learning optimization to improve the performance of the large model. We performed large-scale pretraining and instruction tuning on the big model LLama-2. Based on the highquality and richly typed training data automatically constructed by ChatGPT, remote supervision, and other algorithms, which contains a total of 1 million entities, relations, and events, we designed corresponding English templates for instruction tuning. Second, we performed supervised fine-tuning of the model using manually labeled high-quality training sets. To further improve the model performance, we innovatively adopt a multi-task learning optimization strategyGradNorm, which can dynamically adjust the weights of different tasks, thus balancing the losses among tasks during the training process and reducing the overall training loss. After information extraction experiments, our model is compared with other models to test the performance of uniform information extraction for large models, and the experimental results show that our model performs well.

关键词： Large language Model Uniform information Extraction Instruction Fine-tuning Multi-task Learning Optimization

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Digital Human Techniques for Education Reform 24

Digital Human Techniques for Education Reform

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7th international conference on Educational technology Management, ICETM 2024

作者： Guo, Peirong Zhang, Qi Tian, Chunwei Xue, Wanli Feng, Xiaocheng School of Software Northwestern Polytechnical University Shaanxi Xi’an China School of Economics and Management Harbin Institute of Technology at Weihai Shandong Weihai China Shenzhen Research Institute of Northwestern Polytechnical University Northwestern Polytechnical University Guangdong Shenzhen China School of Computer Science and Engineering Tianjin University of Technology Tianjin China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology Heilongjiang Harbin China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Guangdong Shenzhen China

ISBN: (纸本)9798400717468

the rapid evolution of artificial intelligence, big data, and generative AI models has ushered in significant transformations across various sectors, including education. Digital Human Technique, an innovative technology grounded in advanced computer science and artificial intelligence, is reshaping educational paradigms by enabling virtual humans to simulate human behavior, express emotions, and interact with users. this paper explores the application of Digital Human Technique in education reform, focusing on creating immersive, intelligent classroom experiences that foster meaningful interactions between teachers and students. We define Digital Human Technique and delve into its key technical components such as character modeling and rendering, natural language processing, computer vision, and augmented reality technologies. Our methodology involves analyzing the role of educational digital humans created through these technologies, assessing their impact on educational processes, and examining various application scenarios in educational reform. Results indicate that Digital Human Technique significantly enhances the learning experience by enabling personalized teaching, increasing engagement, and fostering emotional connections. Educational digital humans serve as virtual teachers, interactive learning aids, and facilitators of emotional interaction, effectively addressing the challenges of traditional educational methods. they also promote a deeper understanding of complex concepts through simulated environments and interactive digital content. © 2024 Copyright held by the owner/author(s).

关键词： Digital elevation model

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Native vs Non-native language Prompting: A Comparative Analysis 25th

Native vs Non-native Language Prompting: A Comparative Analy...

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25th international conference on web information Systems Engineering, WISE 2024

作者： Kmainasi, Mohamed Bayan Khan, Rakif Shahroor, Ali Ezzat Bendou, Boushra Hasanain, Maram Alam, Firoj Qatar University Doha Qatar University of Doha for Science and Technology Doha Qatar Liverpool John Moores University Doha Qatar Carnegie Mellon University in Qatar Ar-Rayyan Qatar Qatar Computing Research Institute Ar-Rayyan Qatar

ISBN: (纸本)9789819605750

Large language models (LLMs) have shown remarkable abilities in different fields, including standard Natural language processing (NLP) tasks. To elicit knowledge from LLMs, prompts play a key role, consisting of natural language instructions. Most open and closed source LLMs are trained on available labeled and unlabeled resources-digital content such as text, images, audio, and videos. Hence, these models have better knowledge for high-resourced languages but struggle with low-resourced languages. Since prompts play a crucial role in understanding their capabilities, the language used for prompts remains an important research question. Although there has been significant research in this area, it is still limited, and less has been explored for medium to low-resourced languages. In this study, we investigate different prompting strategies (native vs. non-native) on 11 different NLP tasks associated with 11 different Arabic datasets (8.7K data points). In total, we conducted 198 experiments involving 3 open and closed LLMs (including an Arabic-centric model), and 3 prompting strategies. Our findings suggest that, on average, the non-native prompt performs the best, followed by mixed and native prompts. All prompts will be made available to the community through the LLMeBench (https://***/) framework. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Translation (languages)

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Automating information Retrieval with Semantic Data processing

Automating Information Retrieval with Semantic Data Processi...

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international Youth conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Liana G. Amirkhanyan Alla E. Brom Industrial logistic department Bauman Moscow State Technical University Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

this report proposes to consider the possibility of automating the search for the necessary information in a corporate database that may contain data of various formats. To implement such a process, it is necessary to use large language models (LLM), but to generate high-quality responses, it is necessary to further train the models using Retrieval-Augmented Generation (RAG) technology, which creates vector databases based on corporate data. these repositories can be constantly updated to assist generative Artificial Intelligence (AI) in providing optimal contextual and timely responses, in addition to connecting the semantic ranking of the results obtained for enhanced accuracy. the work also proposes to evaluate the work of language models using known metrics. this is a result of studying which the problem of the occurrence of errors of the 1st and 2nd kind when generating a response by the LLM model is revealed. Consequently, it has been determined that no universal assessment of models exists.

关键词： Analytical models Adaptation models Accuracy Statistical analysis Large language models Semantics Search engines Data processing Data models Vectors

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A Complex Network-based Dialogue Hybrid Intelligent information System for Solving Tasks in thermal Power Engineering

A Complex Network-based Dialogue Hybrid Intelligent Informat...

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international Youth conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Evgeny A. Belousov Danila R. Gromozdov Dmitry V. Aladin Marina O. Ponomareva Nikita S. Klimov Nikolay M. Gorkunov Information Processing and Management Systems Department Bauman Moscow State Technical University Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

this paper presents a complex network-based dialogue Hybrid Intelligent information System (HIIS) that utilizes complex networks to solve tasks in the field of thermal power engineering. As the volume of data in this area increase, the need for advanced tools that facilitate problem solving and enhance operational efficiency becomes evident. the proposed HIIS integrates large language models (LLMs) with a robust architecture that enables natural language interaction while effectively addressing complex technical tasks. the system's architecture includes three components: input, output, and a complex network-based inference, ensuring seamless communication and precise data analysis. Despite the advantages of LLMs, the paper discusses challenges such as model hallucination and the “black box” phenomenon, which are addressed to ensure transparency and reliability. this innovative approach aims to provide practical recommendations and insights, ultimately assisting professionals in making informed decisions in the dynamic field of thermal power engineering.

关键词： Power engineering Data analysis Rapid thermal processing Large language models thermal engineering Complex networks Power industry Hybrid power systems Reliability information systems

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Multilingual Sign language Dictionary Development for Clinical Use

Multilingual Sign Language Dictionary Development for Clinic...

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international Youth conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Arifa Ashrafi Viktor S. Mokhnachev Diem H. Chung Ali Makhmud Waseem Bacha Department of Infocognitive Technologies Moscow Polytechnic University Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

Getting the right medical care can be extremely difficult for patients with hearing impairment. Particularly clinics in underdeveloped countries where there are budgetary limitations, a shortage of qualified service providers, and a lack of access to modern healthcare technology. Developing a multilingual dictionary for clinical use can improve the communication between healthcare providers, and hearing impaired patients. the use of medical terminology in this dictionary can reduce the misunderstandings between patients and healthcare providers. the purpose is to enhance patient care, ensure accurate information exchange. the method of creating a multilingual sign language dictionary has been demonstrated in this article. Real-time motion tracking and machine learning have been applied in this approach. the research methodology outlines not just how to develop the dictionary as a mobile application and a web application but also ways to design it user-friendly alongside how to use it for reaching as many people as possible. Preliminary studies had taken place to use it as a test program. Four key hypotheses have been examined, and their implementations are described. this research mainly highlights the importance of combining technology and accessibility to address the unique needs of the hearing-impaired community. the focus is on offering a practical solution for clinical environments worldwide.

关键词： Sign language Power engineering Dictionaries Tracking Scalability Medical services Auditory system Real-time systems Multilingual Mobile applications

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Improving Text Classification by Fusing Linguistic and Semantic Features

Improving Text Classification by Fusing Linguistic and Seman...

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international conference on Advancements in Computational Sciences (ICACS)

作者： Sarang Shaikh Ehtesham Hashmi Sule Yildirim Yayilgan Mohamed Abomhara Rajendra Akerkar Department of Information Security and Communication Technology (IIK) Norwegian University of Science and Technology (NTNU) Gjøvik Norway Big Data & Emerging Technologies Group Western Norway Research Institute Sogndal Norway

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

Text classification remains a fundamental challenge in natural language processing (NLP), with performance often limited by the reliance on either traditional linguistic features or semantic embedding techniques in isolation. this study addresses this limitation by proposing a feature fusion method that integrates traditional linguistic features—such as part-of-speech tags, bag-of-words, TF-IDF, and n-grams—with advanced semantic embedding techniques like word 2 vec and doc 2 vec. the proposed approach aims to capture both syntactic and semantic nuances, enhancing the robustness and accuracy of text classification tasks. To evaluate its effectiveness, the method was applied to five datasets across three critical domains: fake news detection, bloom’s taxonomy classification, and hate speech detection. Key performance metrics, including accuracy, precision, recall, and f1-score, were used to assess the performance of the proposed approach. the experimental results demonstrate that the fusion of linguistic and semantic features consistently outperforms compared to using the each feature type alone, achieving accuracies of $\mathbf{7 9 \%}$ and $\mathbf{6 7 \%}$ for fake news datasets, $38 \%$ and $\mathbf{6 4 \%}$ for bloom’s taxonomy datasets, and $\mathbf{7 0 \%}$ for hate speech datasets. the findings highlight the proposed method’s ability to bridge syntactic and semantic gaps, offering a robust solution for improving text classification performance across diverse NLP applications.

关键词： Accuracy Semantics Text categorization Taxonomy Hate speech Linguistics Syntactics Natural language processing Robustness Fake news

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Data Augmentation of Gujarati Texts for Sentiment Analysis 4th

Data Augmentation of Gujarati Texts for Sentiment Analysis

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4th international conference on advanced Network Technologies and Intelligent Computing, ANTIC 2024

作者： Desai, Nikita Dabhi, Vipul Department of Information Technology Dharmsinh Desai University Gujarat Nadiad India

ISBN: (纸本)9783031837920

Sentiment analysis (SA) is a crucial natural language processing (NLP) tool for understanding user sentiments, particularly in business intelligence. However, the lack of labeled data in low-resource languages like Gujarati limits the effectiveness of such models. We propose a novel data augmentation pipeline specifically designed for Gujarati movie review datasets to balance the class distribution. this paper presents manually tagged SA datasets for Gujarati movie reviews, annotated with three sentiment classes: positive, negative, and neutral. IndoWordNet is used to generate new data points for under-represented classes by replacing words with their synonyms, while IndicBERT embeddings are utilized to verify the semantic similarity of the augmented sentences. After balancing the dataset, standard classifiers such as SVM, Naive Bayes, Logistic Regression, and KNN were used to evaluate improvements in sentiment classification performance. An increase of 21% in the macro F1 score was empirically observed when the augmented balanced dataset was used for the classification task. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantics

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