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HyPRETo: Hybrid Pre-trained Ontology Approach for Contextual Relation Classification on Mosquito Vector Biocontrol Agents 8th

HyPRETo: Hybrid Pre-trained Ontology Approach for Contextual...

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8th IFIP TC 12 international conference on Computer, Communication and Signal processing, ICCCSP 2024

作者： Jeyakodi, G. Bala, P. Shanthi Department of Computer Science School of Engineering and Technology Pondicherry University Puducherry India

ISBN: (纸本)9783031736162

Pre-trained language Model facilitates contextual relation classification by capturing contextual information, addressing word ambiguity, encoding global sentence context, enabling transfer learning, handling out-of-vocabulary words, and improving performance with limited labelled data. Existing pre-training approaches suffer in size, bias, interpretability, generalization, and the lack of domain specificity. To address this, HyPRETo, the hybrid model that combines the strength of token replacement and dynamic masking is proposed to achieve upgraded performance to increase classification accuracy. The Mosquito Vector Biocontrol Agents data is used for implementing the model for a contextual relation classification task. HyPRETo uses ontology to provide structured knowledge. HyPRETo is pre-trained by ELECTRA and fine-tuned by RoBERTa models. Feedforward and softmax activation function is used for classification. The natural language processing technique and SQL database are used to develop an automated question-answering system. The HyPRETo was evaluated with state-of-art models and achieved 98.42% accuracy. As a contribution, the manually annotated input dataset on the mosquito vector control agent is prepared for the classification task. Subsequently, the enhanced model is developed. The interface for an automated question-answering system for mosquito vector biocontrol agents is developed to assist public health applications such as mosquito vector control, disease control, ecosystem management, environmental conservation, and so on. © IFIP international Federation for Information processing 2025.

关键词： Vectors

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Information Retrieval System Based on the knowledge Extraction

Information Retrieval System Based on the Knowledge Extracti...

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

作者： Ilya V. Kolomichyk Vadim V. Dzitiev Anton I. Kanev Bauman Moscow State Technical University Moscow Russia

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

The article discusses stages in developing a method for solving the search problems in the natural language processing. It reveals relevance of the topic. The article proposes a method for optimizing the search for the most relevant documents using the semantic knowledge graph in the context of modern approaches to the natural language processing. It describes the main stages aimed at improving the accuracy in searching for documents that match a complex user query. Results of the information retrieval algorithm operation are analyzed, and strengths and weaknesses of the approach are highlighted. The article provides theoretical computation of increasing the quality. Efficient introduction of the proposed method could improve the quality of searching for the composite entities.

关键词： knowledge engineering Power engineering Accuracy Large language models Semantics knowledge based systems knowledge graphs Information retrieval Search problems natural language processing

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FinNLP-FNP-LLMFinLegal-2025 Shared Task: Financial Misinformation Detection Challenge Task 9

FinNLP-FNP-LLMFinLegal-2025 Shared Task: Financial Misinform...

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： Liu, Zhiwei Wang, Keyi Bao, Zhuo Zhang, Xin Dong, Jiping Yang, Kailai Kabir, Mohsinul Giannouris, Polydoros Xing, Rui Park, Seongchan Kim, Jaehong Li, Dong Xie, Qianqian Ananiadou, Sophia University of Manchester United Kingdom Artificial Intelligence Research Center Japan Columbia University United States Northwestern University United States Internet Domain Name System Beijing Engineering Research Center Co. China University of Chinese Academy of Sciences China University of Melbourne Australia Korea Republic of The FinAI United States Archimedes Athena RC Greece

ISBN: (纸本)9798891762091

Despite the promise of large language models (LLMs) in finance, their capabilities for financial misinformation detection (FMD) remain largely unexplored. To evaluate the capabilities of LLMs in FMD task, we introduce the financial misinformation detection shared task featured at COLING FinNLP-FNP-LLMFinLegal-2024, FMD Challenge. This challenge aims to evaluate the ability of LLMs to verify financial misinformation while generating plausible explanations. In this paper, we provide an overview of this task and dataset, summarize participants’ methods, and present their experimental evaluations, highlighting the effectiveness of LLMs in addressing the FMD task. To the best of our knowledge, the FMD Challenge is one of the first challenges for assessing LLMs in the field of FMD. Therefore, we provide detailed observations and draw conclusions for the future development of this field. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Augmenting Content Retrieval Through Machine Learning 3

Augmenting Content Retrieval Through Machine Learning

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Pavan, P.S. Sripriya, T. Vikas, B. Shivank Parmar, Yuvraj Acharjya, Kalyan Faculty of Engineering and Technology Department of Civil Engineering Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350319125

gadget getting to know is a subset of synthetic intelligence that focuses on making predictions via reading and getting to know patterns from records. In current years, machine studying has been increasingly more carried out to numerous fields consisting of natural language processing, laptop vision, and records analytics. in the field of content material retrieval, system gaining knowledge of strategies are used to enhance the effectiveness and efficiency of search algorithms, making it less complicated for customers to discover the information they ***, content retrieval structures depend on indexing and key-word-based totally strategies to retrieve applicable information. even as these strategies have been powerful, they have barriers in dealing with complicated queries and information the context of the facts being searched for. this is where machine learning is available in, providing the functionality to research and learn from big amounts of facts to enhance the accuracy and relevance of search consequences. through the usage of machine getting to know, content retrieval systems can better recognize the purpose at the back of a person's query and generate more correct outcomes. that is accomplished through strategies such as natural language processing, which allows the system to recognize and extract which means from text and speech. moreover, gadget gaining knowledge of can be carried out to study from past person interactions and feedback to continuously enhance the search enjoy. © 2023 IEEE.

关键词： Machine learning

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Semantic Textual Similarity (STS) in Arabic using Lexical-Semantic Analysis 25

Semantic Textual Similarity (STS) in Arabic using Lexical-Se...

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25th international Arab conference on Information Technology, ACIT 2024

作者： Al-Saiyd, Nedhal A. Al-Sayed, Intisar A. Uruk University College of Technical Engineering Department of Communication Technical Engineering Baghdad Iraq Uruk University College of Engineering Biomedical Engineering Department Baghdad Iraq

ISBN: (纸本)9798331540012

Semantic text similarity has a crucial role in natural language understanding (NLU), which is intended to evaluate the semantic and relatedness of two texts and produce a similarity score. Frequently, the Arabic short text consists of very few words, which mean that they usually do not provide adequate data in understanding the sentences. There are many difficulties and challenges that face processing and understanding of the Arabic short texts. The representations of Arabic short text of different lengths are investigated using lexical-semantic technique to eliminate the less informative words. In this paper, a hybrid approach that combines word-based features and text-based features is proposed for MSA Arabic short texts. It is based on lexical and semantic analysis. Semantic relatedness uses the Feature Vector (FV) Model, which is generated by acquiring semantic features of the word and the context. A comprehensive evaluation is done and the accuracy of the links between concepts is enhanced. The results shows that the lexical and Semantic knowledge is important in understanding short text. © 2024 IEEE.

关键词： Data assimilation

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An Overview of Part-of-Speech Tagging Methods and Datasets for Malay language 8

An Overview of Part-of-Speech Tagging Methods and Datasets f...

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8th IEEE international conference on Software engineering and Computer Systems, ICSECS 2023

作者： Chua, Chi Log Lim, Tong Ming See, Kwee Teck Kuala Lumpur Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9798350310931

The purpose of this review is to summarise the knowledge about Malay Part-of-Speech (POS) training methods and datasets, and to identify its future research directions. A total of ten research papers related to Malay POS model training methods has been reviewed and five datasets were found from online resources. Two major issues were identified - first, of all the ten papers reviewed, it was found that dataset used to train Malay POS were not standardized. Second, limited dataset was found from online resources - only five datasets were available, and only three were annotated. This review highlights two directions worth future investigation: how to train a high-performance POS model using only a small amount of annotated data, and how to utilize existing high-performance POS models to reduce the burden of annotating data. © 2023 IEEE.

关键词： natural language processing systems

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Construction of Large-Scale Chinese-English Bilingual Corpus and Sentence Alignment 2nd

Construction of Large-Scale Chinese-English Bilingual Corpus...

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2nd EAI international conference on Application of Big Data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2022

作者： Jie, Sun School of International Culture and Education Heilongjiang University Heilongjiang Province Harbin150088 China

ISBN: (纸本)9783031239465

With the development of computer and Internet, applications based on bilingual (or multilingual) parallel corpora are increasing in the field of natural language processing. In addition to the application of machine translation, the construction of parallel corpus is also of great value for bilingual dictionary compilation, word meaning disambiguation and cross language information retrieval. At present, the bilingual corpus of word alignment and sentence alignment has a large scale, and the related alignment algorithms are also relatively mature. In contrast, the chunk level alignment algorithm remains to be studied, and the chunk level alignment corpus required by the alignment algorithm is quite lacking. The construction of bilingual corpus and its automatic alignment are of great significance to the development of computational linguistics. At present, the existing bilingual corpora at home and abroad, especially Chinese-English bilingual corpora, are not large, the processing standards are not unified, and there is no general bilingual corpus that can be used publicly. It has laid a solid foundation for the large-scale establishment of bilingual language information and knowledge base with unified standards and norms, multi fields, multi genres and sentence level alignment. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： knowledge based systems

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AI Chatbots as Virtual Teaching Assistants 27th

AI Chatbots as Virtual Teaching Assistants

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27th international conference on Interactive Collaborative Learning, ICL 2024

作者： Kasmaee, Ali Shiraee Mahyar, Hamidreza Faculty of Engineering McMaster University HamiltonON Canada

ISBN: (纸本)9783031835193

Recent advancements in artificial intelligence (AI), particularly in natural language processing (NLP), have led to the development of sophisticated chatbots like ChatGPT. These chatbots excel in answering questions and can significantly improve the learning experience by providing immediate feedback and support to students. They enable continuous learning outside traditional classrooms by understanding and responding to questions with human-like proficiency. However, their responses are limited to the information they were trained on. In this study, we employ the Retrieval-Augmented Generation technique to enable these chatbots to access and retrieve relevant information from external knowledge sources (such as course material) to improve the generation capabilities of these models as virtual teaching assistants. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Chatbots

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ViLCo-Bench: VIdeo language COntinual learning Benchmark 38

ViLCo-Bench: VIdeo Language COntinual learning Benchmark

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38th conference on Neural Information processing Systems, NeurIPS 2024

作者： Tang, Tianqi Deldari, Shohreh Xue, Hao De Melo, Celso Salim, Flora School of Computer Science Engineering University of New South Wales Australia DEVCOM Army Research Laboratory United States

Video language continual learning involves continuously adapting to information from video and text inputs, enhancing a model's ability to handle new tasks while retaining prior knowledge. This field is a relatively under-explored area, and establishing appropriate datasets is crucial for facilitating communication and research in this field. In this study, we present the first dedicated benchmark, ViLCo-Bench, designed to evaluate continual learning models across a range of video-text tasks. The dataset comprises ten-minute-long videos and corresponding language queries collected from publicly available datasets. Additionally, we introduce a novel memory-efficient framework that incorporates self-supervised learning and mimics long-term and short-term memory effects. This framework addresses challenges including memory complexity from long video clips, natural language complexity from open queries, and text-video misalignment. We posit that ViLCo-Bench, with greater complexity compared to existing continual learning benchmarks, would serve as a critical tool for exploring the video-language domain, extending beyond conventional class-incremental tasks, and addressing complex and limited annotation issues. The curated data, evaluations, and our novel method are available at https://***/cruiseresearchgroup/ViLCo. © 2024 Neural information processing systems foundation. All rights reserved.

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A Holistic Exploration of natural language processing: Challenges, Milestones, and Research Frontiers 2

A Holistic Exploration of Natural Language Processing: Chall...

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2nd IEEE international conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Chhabra, Deepshikha Manchanda, Nandita Malik, Meena Chandigarh University Computer Science & Engineering Mohali India

ISBN: (纸本)9798350369960

natural language processing (NLP) is a rapidly evolving field with diverse applications, from chatbots to translation services. This survey offers a comprehensive exploration of NLP, covering its fundamentals, challenges, literature, structured model comparison, and research gaps. The introduction clarifies NLP's core concepts and applications. We then delve into its multifaceted challenges, spanning complex multimodal understanding to ethical issues like bias and fairness. This sets the stage for literature review, highlighting milestones like BERT and GPT-3. We provide a tabular comparison of influential models, elucidating their features, strengths, and limitations. Beyond reviewing past achievements, we spotlight critical research gaps - multimodal understanding, commonsense reasoning, bias mitigation, multilingualism, and ethics. Addressing these gaps is vital for responsible NLP advancement. In summation, paper offers a comprehensive and holistic view of the expansive field of NLP. It skillfully combines introductory concepts, a navigational guide through the realm of challenges, a detailed literature review, structured model comparisons, and a forward-looking perspective on the pressing research gaps. We aspire for this paper to emerge as an invaluable resource, serving as a guiding light for the research pursuits of scholars, the practical endeavors of practitioners, and the policy considerations of stakeholders within the expansive and dynamic NLP community. Its overarching objective is to steer future research initiatives toward the crucial task of addressing these pivotal gaps, thereby propelling the field into new horizons of knowledge and application. © 2023 IEEE.

关键词： BERT Challenges Deep Learning natural language processing Translation

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