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检索条件"任意字段=7th International Conference on Machine Learning and Natural Language Processing, MLNLP 2024"

共 580 条记录，以下是1-10 订阅

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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...

引用

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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the role of ChatGPT in Chinese reading education for Chinese as a heritage language (CHL) learners 24

The role of ChatGPT in Chinese reading education for Chinese...

引用

7th international conference on Big Data and Education, ICBDE 2024

作者： Ge, Hanjie College of Chinese Language and Culture Jinan University Guangzhou China

ISBN: (纸本)9798400716980

With technological advancements and evolving educational needs, traditional methods of Chinese reading instruction face challenges. ChatGPT, a robust natural language processing tool, introduces new possibilities for Chinese reading education. this study utilizes standardized reading proficiency test scores from 446 Chinese heritage language (CHL) learners to investigate the impact of character recognition abilities on their overall Chinese reading proficiency through quantitative analysis. Drawing on empirical findings, the research examines ChatGPT’s specific contributions in teaching Chinese characters and enhancing reading skills tailored for heritage learners. the study aims to explore ChatGPT’s potential as an innovative artificial intelligence language model to enhance efficiency and educational outcomes in Chinese reading instruction, providing recommendations for overseas Chinese heritage language education. © 2024 Copyright held by the owner/author(s).

关键词： Contrastive learning

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Quantitative Analysis of Side Effect Severity using the D-SESS Scale: An Observational Study with Analytical Techniques 24

Quantitative Analysis of Side Effect Severity using the D-SE...

引用

2024 7th international conference on Healthcare Service Management, ICHSM 2024

作者： Gaddam, Vijay Kumar Veldandi, Amulya Godana, Akhila Siri Sripathi, Surya Rao Gudisa, Timothy Department of BioMedical Engineering and Informatics Indiana University IndianapolisIN United States

ISBN: (纸本)9798400710162

the study presented in this paper introduces the Drug Side Effect Severity Scale (D-SESS), a novel quantitative metric designed to assess the severity of drug side effects. By applying natural language processing (NLP) techniques like tokenization for preprocessing and developing clustering and predictive models using machine learning algorithms, this research aims to provide a more nuanced understanding of drug side effects. this categorizes side effects into mild, moderate, and severe based on their impact on patient health. this paper details the methodology behind creating the D-SESS, its initial validation, and the application of clustering models such as K-Means, and Agglomerative Clustering to organize drugs based on their side effect profiles. Predictive modeling further enhances the utility of D-SESS by enabling the assignment of drugs to clusters, facilitating a comprehensive analysis of drug safety. the Drug Side Effect Severity Scale (D-SESS) aims to address this gap by introducing a quantitative metric designed to offer a more nuanced understanding of drug side effects. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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ParaFusion-Extended: Large Scale Paraphrase Dataset Integrating Lexico-Phrasal Knowledge 30th

ParaFusion-Extended: Large Scale Paraphrase Dataset Integrat...

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30th international conference on Computational and Experimental Engineering and Sciences

作者： Jayawardena, Lasal Yapa, Prasan Robert Gordon Univ Garthdee HouseGarthdee Rd Aberdeen AB10 7AQ Scotland Inst Informat Technol 57 Ramakrishna Rd Colombo 00600 Sri Lanka

ISBN: (纸本)9783031774911;9783031774898;9783031774881

Paraphrasing, the art of rephrasing text while retaining its original meaning, lies at the core of natural language understanding and generation. With the rise of demand for more domain-specialized models, high-quality data is more valued than ever;this includes paraphrasing. ParaFusion-Extend (PFE) is a large-scale dataset driven by Large language Models incorporating lexical and phrasal knowledge. the dataset is curated to contain high-quality diverse paraphrase pairs and also separate knowledge bases that could be used for research work and data augmentation models. We show that PFE offers around at least a 30% increase in syntactic and lexical diversity compared to the original data sources that are commonly used. We demonstrate the effectiveness of PFE on several downstream tasks such as few-shot learning and training on sentence embeddings. We utilize a gold-standard evaluation scheme, which is further strengthened by human evaluation that shows the potential of PFE in advancing paraphrase generation.

关键词： paraphrase generation natural language processing natural language generation knowledge representation large language models data augmentation sentence embeddings few-shot learning

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language Detection for Global Communication 6th

Language Detection for Global Communication

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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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Ensemble machine learning and natural language processing Techniques for the Assessment of Depression Using Non-clinical Texts 12th

Ensemble Machine Learning and Natural Language Processing T...

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12th international conference on Big Data Analytics, BDA 2024

作者： Saraswat, Akshat Arya, Pushkar Prakash Guru Gobind Singh Indraprastha University East Delhi Campus Surajmal Vihar New Delhi110032 India National Institute of Technology Patna Bihar Patna India

ISBN: (纸本)9783031861925

this paper explores a novel ensemble machine learning-based approach for indicating mental health in terms of depression and depression-related states among users by considering the Reddit dataset. Reddit being one of the bigger social media platforms, provides a rich source of data along with anonymity where individuals can openly share their thoughts without the fear of judgment. Utilizing natural language processing (NLP) methods and ensemble machine learning algorithms, our models capture and analyze patterns in the user’s language that signify mental health indicators, and we compare the different boosting ensemble machine models such as XGBoost, AdaBoost, CatBoost, etc., to assess the accuracy of prediction of mental health. the proposed method involves the following steps (i) data collection, (ii) data pre-processing, (iii) Implementing boosting algorithms and provides the gradation of data sources and techniques that have been used for mental health care and monitoring. through our method, we demonstrate measurable accuracy in identifying depressive and depression-related content yielding promising quantitative results that support its potential application for mental health monitoring and intervention. Unigrams when combined with all the ensemble learning methods such as XGBoost, CatBoost, AdaBoost, and MART gave us more than 0.93 accuracy always, Bigrams when combined with all the ensemble methods gave us at least 0.84 accuracy in all cases and similarly Latent Dirichlet Allocation (LDA) yielded an accuracy of greater than equal to 0.90 in all cases. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Towards Building a Robust Knowledge Intensive Question Answering Model with Large language Models 13th

Towards Building a Robust Knowledge Intensive Question Answe...

引用

13th international conference on natural language processing and Chinese Computing

作者： Hong, Xingyun Shao, Yan Wang, Zhilin Duan, Manni Jin, Xiongnan Zhejiang Lab Hangzhou Peoples R China Hangzhou Res & Dev Ctr China Mobile Hangzhou Peoples R China

ISBN: (纸本)9789819794300;9789819794317

the development of LLMs has greatly enhanced the intelligence and fluency of question answering, while the emergence of retrieval enhancement has enabled models to better utilize external information. However, the presence of noise and errors in retrieved information poses challenges to the robustness of LLMs. In this work, to evaluate the model's performance under multiple interferences, we first construct a dataset based on machine reading comprehension datasets simulating various scenarios, including critical information absence, noise, and conflicts. To address the issue of model accuracy decline caused by noisy external information, we propose a data augmentation-based fine-tuning method to enhance LLM's robustness against noise. Additionally, contrastive learning approach is utilized to preserve the model's discrimination capability of external information. We have conducted experiments on both existing LLMs and our approach, the results are evaluated by GPT-4, which indicates that our proposed methods improve model robustness while strengthening the model's discrimination capability.

关键词： Retrieval-augmented LLM Question answering Robustness

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Enhancing Emotional Support Conversation with Cognitive Chain-of-thought Reasoning 13th

Enhancing Emotional Support Conversation with Cognitive Chai...

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13th international conference on natural language processing and Chinese Computing

作者： Cao, Yaru Chen, Zhuang Bi, Guanqun Feng, Yulin Chen, Min Wan, Fucheng Huang, Minlie Yu, Hongzhi Northwest Minzu Univ Dept Chinese Language & Literature Lanzhou 730030 Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Northwest Minzu Univ Key Lab Linguist & Cultural Comp Minist Educ Lanzhou 730030 Peoples R China

ISBN: (纸本)9789819794300;9789819794317

Emotional support conversation can reduce mental stress and provide social benefits. However, the help seeker's mental state often lies beneath the surface utterance in conversation, making it difficult to understand the seeker's deep status and provide effective emotional support through mere dialogue modeling. To address this challenge, we propose CogChain, a cognitive chain-of-thought (CoT) reasoning framework that mimics a human supporter's cognitive process for emotional support conversation. Specifically, CogChain employs a chained structure to analyze the seeker's issues, infer internal thoughts, determine behavioral intentions, and select appropriate strategies to achieve support goals. We further design an in-context learning pipeline using large language models to efficiently generate CogChain for any given dialogue content. To validate the effectiveness of CogChain, we incorporate it in the frequently used ESConv dataset and accordingly train enhanced machine supporters with CoT reasoning ability. Extensive automatic and human evaluations show that, CogChain not only improves the machine supporter's performance for in-domain seen scenarios but also enhances its generalizability to out-of-domain unseen scenarios, demonstrating the importance of cognitive reasoning for emotional support conversation (Our resources are available at https://***/YaruCao-AI/CogChain).

关键词： Emotional Support Conversation Chain-of-thought Reasoning Large language Models

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the Research on the Application Modes of Intelligent Instructional Design Generation in Primary and Secondary Schools Supported by Large Models 24

The Research on the Application Modes of Intelligent Instruc...

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7th international conference on Educational Technology Management, ICETM 2024

作者： Lin, Jian Wang, Xiaoyi Li, Bingjun He, Musheng Luo, Yufei School of Computer and Intelligent Education Lingnan Normal University Guangdong Zhanjiang China Guangdong Provincial Key Laboratory of Development and Education for Special Needs Children Lingnan Normal University Guangdong Zhanjiang China School of Electronic and Electrical Engineering Lingnan Normal University Guangdong Zhanjiang China

ISBN: (纸本)9798400717468

As the development of large language models and generative artificial intelligence technologies continues to advance, the application of digital teaching resources is exhibiting a new trend of human-machine collaboration and co-creation. As an important digital teaching resource for the implementation of smart teaching, the application of instructional design documents has been significantly influenced by the development of intelligent technologies, especially in the field of primary and secondary education, which holds significant research value. However, there is a clear gap between the value claims and actual effects of traditional instructional design, which limits the professional development of teachers and hinders the comprehensive development of students. this research aims to combine ChatGPT-4 models and generative artificial intelligence technology to construct an intelligent instructional design system platform, achieving the intelligent transformation and upgrading of instructional design. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Analyzing Social Media Text for Myer-Briggs Personality Type Prediction 12th

Analyzing Social Media Text for Myer-Briggs Personality Type...

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12th international conference on Recent Trends in Computing, ICRTC 2024

作者： Bhargava, S. Ramyadevi Julian, Anitha Rele, Mayur Mary, Gerardine Immaculate Udaya Krishnan, M. Saveetha Engineering College Chennai Thandalam India Parachute Health PrincetonNJ United States Ellore Institute of Technology Vellore India

ISBN: (纸本)9789819789450

this research aims to create a text-based personality prediction system by utilizing machine learning and natural language processing methods. this methodology allows automatic (MBTI) personality types of prediction from the textual data extracted from social media profiles contrary to traditional methods where questionnaire is used. the goal is to classify a person’s psychological personality type from text based social signals captured from their social media profiles. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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