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检索条件"任意字段=6th International Conference on Machine Learning and Natural Language Processing, MLNLP 2023"

共 688 条记录，以下是61-70 订阅

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ARC-NLP at Multimodal Hate Speech Event Detection 2023: Multimodal Methods Boosted by Ensemble learning, Syntactical and Entity Features 6

ARC-NLP at Multimodal Hate Speech Event Detection 2023: Mult...

引用

6th Workshop on Challenges and Applications of Automated Extraction of Socio-Political Events from Text, CASE 2023

作者： Sahin, Umitcan Kucukkaya, Izzet Emre Ozcelik, Oguzhan Toraman, Cagri Aselsan Research Center Ankara Turkey

ISBN: (纸本)9789544520892

Text-embedded images can serve as a means of spreading hate speech, propaganda, and extremist beliefs. throughout the Russia-Ukraine war, both opposing factions heavily relied on text-embedded images as a vehicle for spreading propaganda and hate speech. Ensuring the effective detection of hate speech and propaganda is of utmost importance to mitigate the negative effect of hate speech dissemination. In this paper, we outline our methodologies for two subtasks of Multimodal Hate Speech Event Detection 2023. For the first subtask, hate speech detection, we utilize multimodal deep learning models boosted by ensemble learning and syntactical text attributes. For the second subtask, target detection, we employ multimodal deep learning models boosted by named entity features. through experimentation, we demonstrate the superior performance of our models compared to all textual, visual, and text-visual baselines employed in multimodal hate speech detection. Furthermore, our models achieve the first place in both subtasks on the final leaderboard of the shared task. © CASE 2023 - Proceedings of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-Political Events from Text, associated with 14th international conference on Recent Advances in natural language processing, RANLP 2023.

关键词： natural language processing systems

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A Study of machine learning Dynamics: Algorithms, Applications, and Fundamental Frameworks 6th

A Study of Machine Learning Dynamics: Algorithms, Applicatio...

引用

6th international conference on communication and computational technologies, ICCCT 2024

作者： Shainky Ambhaikar, Asha Kalinga University Chhattisgarh Raipur India

ISBN: (纸本)9789819774258

this review explores the profound influence that machine learning has had on artificial intelligence, affecting various industries such as healthcare, finance, natural language processing, and data utilization and decision-making. It clarifies terms such as "deep learning" and "neural networks" while navigating the complex world of machine learning and demystifying concepts, including supervised, unsupervised, and reinforcement learning. the review provides a critical assessment of algorithmic platforms, shedding light on their historical foundations and practical implementations. Acting as an authority for scholars and a valuable asset for practitioners in the field, it reveals instances of accomplishment, tackles obstacles, and stimulates additional progressions. this brief investigation delineates the domain of computational prowess and mathematical marvels that propel the contemporary era of machine learning. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Harnessing In-Context learning for Personality Elicitation in Large language Models 6

Harnessing In-Context Learning for Personality Elicitation i...

引用

6th international conference on Frontier Technologies of Information and Computer, ICFTIC 2024

作者： Zhang, Haoyang The Hong Kong Polytechnic University Department of Applied Mathematic Hong Kong

ISBN: (纸本)9798331541750

With the rapid advancements in deep learning and natural language processing, large language models (LLMs) have demonstrated remarkable performance across a variety of tasks. While previous studies have explored the existence of personality traits within LLMs, the potential to systematically control and induce specific personalities remains under-researched. this paper introduces a novel approach using In-Context learning (ICL) to induce personality traits in LLMs, specifically grounded in the Big Five personality theory. We present a curated example set designed to evoke each of the five key personality dimensions: openness, conscientiousness, extraversion, agreeableness, and neuroticism. Using the machine Personality Inventory (MPI), we quantitatively measure LLMs' personality traits and analyze the effects of example complexity, relevance, and diversity on the stability and effectiveness of personality induction. Experimental results indicate that LLMs can exhibit personality traits similar to humans and that the ICL method significantly enhances the prominence of these traits. Additionally, LLMs exhibit strong generalization capabilities in scenario-based tests, suggesting their potential for personalized AI applications. this study advances the understanding of personality control in LLMs and highlights the implications for human-AI interaction. © 2024 IEEE.

关键词： Contrastive learning

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Systematic Review on Real-Time Students Behavior Monitoring using machine learning 6

Systematic Review on Real-Time Students Behavior Monitoring ...

引用

6th international conference on Inventive Computation Technologies, ICICT 2023

作者： Akash, S. Dinesh, V. Muthamil Selvan, S. Srikanth, R. Department of Computer Science and Engineering Faculty of Engineering Karpagam Academy of Higher Education Tamil Nadu Coimbatore India

ISBN: (纸本)9798350398496

Student behavior analysis is an important research area that aims to understand and improve student outcomes. machine learning has emerged as a powerful tool for analyzing student behavior, allowing researchers to identify important patterns and predict future behavior. Further, student behavior analysis can help to identify individual student needs and preferences, allowing for personalized learning experiences that are tailored to the unique needs of each student. this can improve engagement and motivation, and help students reach their full potential. this study provides an overview of various methods for student behavior analysis using machine learning, including clustering, classification, time-series analysis, recommender systems, and natural language processing. Moreover, steps involved in using these methods, including data collection, preprocessing, feature engineering, model training, and model evaluation is elaborated. Furthermore, discussion on the architecture, and ethical considerations for using machine learning in student behavior analysis depicted. Finally, the article highlights the importance of carefully choosing the appropriate method for each research question and considering the potential impact of machine learning on students and society. © 2023 IEEE.

关键词： machine learning

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Research on the Application Algorithm of natural language processing Technology in Text Analysis 6

Research on the Application Algorithm of Natural Language Pr...

引用

6th IEEE international conference on Civil Aviation Safety and Information Technology, ICCASIT 2024

作者： Wang, Qiuzi Xi'an Eurasia University Global Language Center Xi'an China

ISBN: (纸本)9798350389418

First, this study constructed a text encoding strategy based on Word2Vec, which laid the data foundation for subsequent classification and sentiment interpretation by converting the corpus into the vector field. then, this paper introduced multiple text classification technologies including support vector machine (SVM) and convolutional neural network (CNN), combined with various feature extraction methods to achieve text classification. this study further optimized the sentiment analysis algorithm, focusing on the accurate identification of positive and negative emotions, and adopted the collaborative operation of sentiment tendency vocabulary and deep learning framework to improve the accuracy of sentiment identification. this paper selected multiple sets of publicly available data sets to perform experiments to evaluate the performance of various algorithms. the experimental results show that the text classification scheme based on Word2Vec and CNN has achieved more than 85 % accuracy, precision and recall, and the accuracy of sentiment analysis is as high as 8 7. 6 %. the above data strongly proves that the algorithm designed in this study shows excellent performance in processing text analysis tasks. through detailed data analysis, this study confirms the feasibility and practical application value of natural language processing technology in text classification and sentiment analysis scenarios. © 2024 IEEE.

关键词： Convolutional neural networks

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NLP Unleashed: Transforming Employment Landscapes with Dynamic Recruitment Platforms 6

NLP Unleashed: Transforming Employment Landscapes with Dynam...

引用

6th international conference on Computational Intelligence and Communication Technologies, CCICT 2024

作者： Jogi, Sanjay Warang, Sanchit Bhor, Harsh Namdev Solanki, Dhruv Patanwadia, Harsh Department of IT K.J. Somaiya Institute of Technology Mumbai India Department of Information Technology K.J. Somaiya Institute of Technology Mumbai India

ISBN: (纸本)9798350374629

the "Job Recruitment Platform" is an innovative system that revolutionizes the recruitment process by integrating natural language processing (NLP) and advanced interview analysis. It offers dynamic profiles for job seekers, transforming traditional resumes into adaptable documents that highlight skills, provide career recommendations, and facilitate networking. For job providers, the platform streamlines hiring with emotion analysis in video interviews, ensuring authentic candidate responses. machine learning algorithms generate a Job Fit Score, prioritizing the most suitable candidates and improving recruitment efficiency. ©2024 IEEE.

关键词： learning algorithms

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Optimizing System Performance in Punjabi language processing: Revealing Linguistic Nuances and Advancing Computational Solutions 6th

Optimizing System Performance in Punjabi Language Processing...

引用

6th international conference on Recent Innovations in Computing, ICRIC 2023

作者： thakral, Manish Devare, Manoj AIIT-AUM Amity University Navi Mumbai India

ISBN: (纸本)9789819734412

this research addresses the optimization of system performance in Punjabi language processing. Recognizing the distinctive linguistic challenges inherent in Punjabi, we employ advanced natural language processing and machine learning techniques to enhance the accuracy and efficiency of Punjabi language applications. through a comprehensive dataset, we systematically identify linguistic nuances and contextual elements specific to Punjabi, providing valuable insights into the language's intricacies. these extracted features serve as the foundation for developing tailored algorithms, specifically designed to tackle the complexities associated with Punjabi language processing. Our study's primary focus is on Punjabi, a language that has been underexplored in this domain. By incorporating state-of-the-art methodologies, we aim to significantly improve the performance of Punjabi language applications. the outcomes of our research are expected to make noteworthy contributions to advancements in information retrieval, sentiment analysis, and machine translation within the digital landscape. the proposed model, driven by the identified linguistic features, promises to be a key factor in elevating the overall accuracy and efficiency of Punjabi language processing systems. this research thus plays a crucial role in bridging the gap and addressing the specific challenges posed by Punjabi, ultimately enhancing the capabilities of language applications in this linguistic context. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： natural language processing systems

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Data Driven Resume Analyzer for Student Career Optimization 6

Data Driven Resume Analyzer for Student Career Optimization

引用

6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Sarumathi, S. Bhavatharany, E. Mathinesan, P. Sanjaiy, R. Department of Artificial Intelligence and Data Science K.S.Rangasamy College of Technology Tiruchengode India

ISBN: (纸本)9798331522667

the proposed Resume Analyzer System is a machine-learning-powered program that helps job searchers improve their resumes by aligning them with particular job criteria and current industry norms. the system has secure registration and login capabilities, and it uses a MongoDB database to protect sensitive data such as resumes, application histories, and personal information. the system assesses resumes using sophisticated natural language processing (NLP) and machine learning (ML) methods, taking into account crucial variables including language quality, structural organization, skill gaps, and keyword optimization. It makes targeted, actionable suggestions to assist users improve their resumes by highlighting missing parts, underrepresented abilities, and poor keyword use. Unlike standard resume screening systems, which often depend on static and generic models, this system uses dynamic learning capabilities to react to changing hiring patterns by constantly updating its database of resumes and job descriptions. this flexibility enables the system to provide targeted, context-relevant feedback, ensuring that resumes satisfy the sophisticated requirements of current recruiting procedures. the Resume Analyzer's comprehensive architecture allows it to accommodate a broad range of resume formats while also providing real-time, job-specific information to assist job seekers polish their applications and improve their visibility in applicant tracking systems. By addressing existing models' limitations, such as insufficient contextual skill analysis, a lack of format versatility, and generic feedback, the Resume Analyzer System enables candidates to stand out in an increasingly competitive job market, making it an invaluable tool for improving career prospects. © 2025 IEEE.

关键词： Adversarial machine learning

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language Detection for Global Communication 6th

Language Detection for Global Communication

引用

6th international conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Karmakar, Arjya Basu, Sourjabha Roy, Pratyay Singha Das, Suvam Ghosh, Tanmoy Narula Institute of Technology West Bengal Agarpara India

ISBN: (纸本)9789819780891

In the era of globalization, overcoming language barriers is essential for fostering international collaboration. this chapter explores the significance and applications of machine-learning-powered language detection in enabling seamless communication across diverse cultures. language detection systems play a crucial role in identifying the language of text data, facilitating cross-cultural communication, and enhancing user experiences in multilingual environments. the chapter delves into the fundamentals of machine learning and various algorithms such as Multinomial Naive Bayes. Python emerges as a powerful programming language for building robust systems, leveraging its rich ecosystem of machine learning libraries. A case study illustrates the practical implementation of a language detection system in Python, covering data preprocessing, feature extraction, and model training. the applications of language detection span industries, including content moderation, customer support automation, and business insights from multilingual feedback. the chapter also addresses challenges, such as mitigating biases, and explores future directions, highlighting advancements in multilingual natural language processing. Ultimately, this research emphasizes the pivotal role of language detection in global communication and provides insights for leveraging these systems to bridge linguistic divides effectively. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Python

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MDVT: A Multi-modal Fake News Detection Framework based on Vision Transformer 6

MDVT: A Multi-modal Fake News Detection Framework based on V...

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6th international conference on machine learning and natural language processing, mlnlp 2023

作者： Wu, Yuexin Tang, Yi Fan, Chunxiao Liu, Yu School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China Beijing-Dublin International College Beijing University of Technology Beijing China

ISBN: (纸本)9798400709241

the growth of social media in recent years has contributed to the spread of fake news on the Internet. Since multimodal contents such as pictures have a huge impact on the spread of news in social media, researchers are increasingly focusing on the automatic detection of multimodal false news. Existing multimodal approaches disregard the recent development results of vision pretraining, consequently, their visual feature extractor primarily uses early proposed convolutional neural networks, such as VGG and ResNet. Compared with vision Transformers, traditional convolution neural networks have insufficient ability to extract local semantics from images as well as capture the relationships between different regions in images, which may reduce the performance of multimodal methods. To address this issue, we propose a multimodal fake news detection framework based on vision Transformer(MDVT). By experimenting with different multi-modal combination methods and feature extractors on this framework, we get a top-performing which combined MacBERT text features and Swin Transformer image features through the maximum method, named MDVT-MS. Experimental studies on a widely used social media dataset Weibo demonstrate that MDVT-MS outperforms current state-of-the-Art multimodal detection methods by 4.4% in accuracy. this proves the effectiveness of the proposed method and shows that vision Transformer can be used as a new vision backbone for multimodal fake news detection. © 2023 ACM.

关键词： Modal analysis

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