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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71-80 订阅

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PLN-DS 2023 - Proceedings of the Doctoral Symposium on natural language processing from the Proyecto ILENIA, held as part of the 39th Edition of the international conference of the Spanish Society for natural language processing, SEPLN 2023

PLN-DS 2023 - Proceedings of the Doctoral Symposium on Natur...

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6th Doctoral Symposium on natural language processing from the Proyecto ILENIA, PLN-DS 2023

the proceedings contain 14 papers. the topics discussed include: progress in the modeling of violent messages in Spanish social networks;towards an artificial intelligence (AI)-based framework to automatically adapt short stories in Spanish into easier and accessible versions;semantic planning for multilingual fiction generation;automatically designing machine learning models out of natural language;automatic detection of hope speech;mitigating biases in deep learning models: a path towards fairness and inclusivity;commonsense knowledge and controllable techniques for an effective and efficient approach to text generation;enriching natural language processing systems with semantic-pragmatic information through communicative intentions;and exploring readability assessment and text simplification for science documents within a multilingual educational context.

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Can Large language Models Recognize and Respond to Student Misconceptions? 6th

Can Large Language Models Recognize and Respond to Student M...

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6th international conference on Adaptive Instructional Systems (AIS) Held as Part of the 26th international conference on Human-Computer Interaction (HCII)

作者： Smart, Francis Bos, Nathan D. Bos, Jaelyn T. Michigan State Univ E Lansing MI 48824 USA Johns Hopkins Univ Baltimore MD USA Univ Calif Santa Cruz Santa Cruz CA 95064 USA

ISBN: (纸本)9783031606083;9783031606090

Expert human tutors can observe learner mistakes to understand their misconceptions and procedural errors. Highly capable, but opaque large language models have shown remarkable abilities across numerous domains, and may be useful for adaptive instruction in a variety of ways. Working with publicly available data from the National Assessment of Educational Progress, (388 questions selected from 4th, 8th and 12th grade math and science) we examined these three questions: 1) Do language models find the same problems difficult as students do? We found statistically significant, but small similarities in performance that differ somewhat by model. 2) Do language models have the same pattern of errors as students? Our findings reveal that, under the "minimal " prompts, the models often mirror students in choosing the same incorrect answers. However, this alignment decreases when prompt models used "chain of thoughts". 3) Can language models interpret and explain students' wrong answers? We presented frequently-chosen wrong answers toNAEP items to GPT-4 and an experienced science teacher, and compared their explanations. there was a good correspondence between these explanations, with 81% being fully or partially in agreement. Discussion focuses on how these capabilities can be used for test design and adaptive instruction.

关键词： Large language Model Difficulty Prediction Distractor Prediction Distractor Analysis Item Analysis Question Difficulty Large language Models machine learning natural language processing

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Bangla Document Classification Based on machine learning and Explainable NLP 6

Bangla Document Classification Based on Machine Learning and...

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6th international conference on Electrical Information and Communication Technology, EICT 2023

作者： Habibullah, Md. Islam, Md. Shymon Jahura, Fatima Tuz Biswas, Jyoti North Western University Department of Computer Science and Engineering Khulna9100 Bangladesh Khulna University Computer Science and Engineering Discipline Khulna9208 Bangladesh

ISBN: (纸本)9798350370836

Massive digital texts are now accessible, thanks to technological advancement. Any amount of disorganized writing is useless. A high-quality representative corpus of any particular language is essential for research in computational linguistics and natural language processing (NLP). Bangla NLP research is still in its infancy because of the dearth of high-quality public corpus. this paper proposed a newly produced corpus consists of 1,30,307 documents covering 10 categories collected from 11 websites, having 2,94,80,828 tokens and 17,59,085 unique tokens. Seven supervised machine learning methods are explored in this work. Furthermore, Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive explanations (SHAP) are also examined to explain about different model performance. the obtained results show that the Random Forest (RF), Decision Tree (DT) and Support Vector machine (SVM) outperform other models. RF classifier achieves the highest accuracy 99.91% which is better than the existing state-of-the-art methods. © 2023 IEEE.

关键词： Decision trees

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CSECU-DSG @ Causal News Corpus 2023: Leveraging RoBERTa and DeBERTa Transformer Model with Contrastive learning for Causal Event Classification 6

CSECU-DSG @ Causal News Corpus 2023: Leveraging RoBERTa and ...

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

作者： Hossain, Md. Akram Aziz, Abdul Chy, Abu Nowshed Department of Computer Science and Engineering University of Chittagong Chattogram4331 Bangladesh

ISBN: (纸本)9789544520892

Cause-effect relationships play a crucial role in human cognition, and distilling cause-effect relations from text helps in ameliorating causal networks for predictive tasks including natural language-based financial forecasting, text summarization, and question-answering. However, the lack of syntactic clues, the ambivalent semantic meaning of words, and complex sentence structures make it one of the challenging tasks in NLP. To address these challenges, CASE-2023 introduced a shared task 3 with two subtasks focusing on event causality identification with causal news corpus. In this paper, we demonstrate our participant systems for this task. We leverage two transformers models including DeBERTa and Twitter-RoBERTa along with the weighted average fusion technique to tackle the challenges of subtask 1 where we need to identify whether a text belongs to either causal or not. For subtask 2 where we need to identify the cause, effect, and signal tokens from the text, we proposed a unified neural network of DeBERTa and DistilRoBERTa transformer variants with contrastive learning techniques. the experimental results showed that our proposed method achieved competitive performance among the participants’ systems and achieved 4th and 3rd rank in subtasks 1 and 2 respectively. © 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 Multi-Objective Evolutionary Stacking Integration Approach for Survival Prediction in GBM Patients 6

A Multi-Objective Evolutionary Stacking Integration Approach...

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6th international conference on natural language processing (ICNLP)

作者： Du, Fangyuan Chen, Hao Chen, Kunjun Yang, Xuelian Xian Univ Posts & Telecommun Sch Comp Sci & Technol Xian Peoples R China Xian Univ Posts & Telecommun Shaanxi Key Lab Network Data Anal & Intelligent P Xian Peoples R China

ISBN: (纸本)9798350349122;9798350349115

To address the challenge of predicting survival rates for patients with Glioblastoma (GBM) after radiation therapy, we propose a two-stage integrated learning method that is based on Stacking and multi-objective differential evolution algorithm. Our aim is to achieve a balance between accuracy and interpretability. We have created a framework for survival prediction using radiomics features of gliomas that are imaged in subregions. Our method constructs a model for predicting GBM survival and visualizes decision rules that are easy to interpret. the method involves two primary steps: 1) Various combinations of MRI sequences and glioma subregions were evaluated to determine the optimal sequence, and the GBM long and short survival classification task in the first stage of the task was achieved by compressing features using spectral clustering. 2) An evolutionary integrated learning method based on Stacking was proposed. It uses a Random Forest classifier as the basic learning module of the integrated model and relies on the multi-objective optimization algorithm NSGA-II to continuously iterate with the aim of maximizing the regression accuracy while minimizing the model's integration complexity. We used 119 GBM patients in the BraTS2020 dataset as our study data. Based on the five-fold cross-validation, the survival classification Accuracy and Recall of our long- and short-term classification algorithm for GBM patients were 0.981 and 0.943, respectively;and the Mean-Square Error (MSE) of the survival prediction of GBM patients with the stack-based evolutionary integration method was 55713.08. the experimental results showed that our proposed method for GBM survival days prediction is significantly better than existing machine learning (ML) methods. this finding enables us to stratify patients more effectively and provides a more reliable basis for achieving personalized treatment for GBM patients.

关键词： glioblastoma survival prediction stacking multi-objective optimization integrated learning

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Detecting Fake News Using machine learning Based Approaches 6

Detecting Fake News Using Machine Learning Based Approaches

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6th international conference on Information and Computer Technologies, ICICT 2023

作者： Edwards, Ty Noel, Ridwan Rashid Texas Lutheran University Department of Math and Computer Science SeguinTX United States

ISBN: (纸本)9798350300956

the spread of false information, commonly known as 'fake news,' has become a significant problem in recent years, with the potential to mislead the public and influence important decisions. In this research, we focus on creating an automated system for fake news detection using natural language processing of news texts. We investigate different machine learning-based classification techniques to predict whether a text is a real or fake news. We utilized popular datasets from Kaggle and implemented Logistic Regression, Support Vector machine, decision tree, k-Nearest Neighbors, multinomial naïve Bayes, and Multilayer Perceptron, as well as an ensemble technique called stacking, which utilizes the other models in its prediction. We also perform a comparative analysis of the accuracies of the different techniques in fake news detection. From our experimental analysis, we found that the ensemble learner, Support Vector machine, and Multilayer Perceptron outperform the other approaches and have the highest overall accuracies. © 2023 IEEE.

关键词： learning systems

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VerbaVisor@Multimodal Hate Speech Event Detection 2023: Hate Speech Detection using Transformer Model 6

VerbaVisor@Multimodal Hate Speech Event Detection 2023: Hate...

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

作者： Esackimuthu, Sarika Balasundaram, Prabavathy Department of Computer Science and Engineering Sri Sivasubramaniya Nadar College of Engineering Tamil Nadu Chennai603110 India

ISBN: (纸本)9789544520892

thapa et al. (2023) task focuses on identifying hate speech or not from text-embedded images and also identify the targets of hate speech. Hate speech detection has emerged as a critical research area in recent years due to the rise of online social platforms and the proliferation of harmful content targeting individuals or specific groups. this task highlights the importance of detecting hate speech in text-embedded images. By leveraging deep learning models, this research aims to uncover the connection between hate speech and the entities it targets. © 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： Speech recognition

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A Novel Energy Saving Algorithm for Network Deep learning Tasks 6

A Novel Energy Saving Algorithm for Network Deep Learning Ta...

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6th international conference on Next Generation Data-Driven Networks, NGDN 2024

作者： Wang, Xin Wang, Yue Cheng, Chen Xie, Zhipu Gao, Wei Cheng, Xinzhou Xu, Lexi Tan, Yongtao Tong, Jing China Unicom Research Institute Beijing China The Intelligent Network Innovation Center of China Unicom Beijing China China Unicom Hainan Branch Hainan China

ISBN: (纸本)9798350388374

Recently, deep learning algorithm brings a lot of breakthroughs in network data analysis, computer vision and natural language processing tasks, such as network root cause diagnosis, image classification, image caption, natural language understanding and so on. To solve network tasks, the AI computing centers are built in each province. However, the rapid deployment of AI computing centers has presented enormous challenges that heightened energy consumption and carbon emissions. To counter this trend, this paper introduces an energy-saving strategy implemented on a specialized platform, designed to cultivate environmentally conscious and low-carbon AI computing centers. this strategy encompasses an exhaustive overview of green energy-saving methodologies, delineating experimental setups, equipment, and configurations utilized. Empirical results underscore a notable 10% increase in scheduled GPU utilization rates, culminating in an overall reduction of 100W in energy consumption and achieving daily energy savings exceeding 10%. Moreover, this paper offers pragmatic insights into implementing this algorithm in practical network scenarios, empowering enterprises to enhance production efficiency, foster product innovation, and concurrently diminish their carbon footprint. © 2024 IEEE.

关键词： Deep learning Energy Saving GPU machine learning Network Data Analysis

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Proceedings of the 6th international conference on Finance, Economics, Management and IT Business, FEMIB 2024

Proceedings of the 6th International Conference on Finance, ...

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6th international conference on Finance, Economics, Management and IT Business, FEMIB 2024

ISBN: (纸本)9789897586958

the proceedings contain 14 papers. the topics discussed include: what do customers demand? inclusive and sustainable entrepreneurial marketing;applications of artificial intelligence in sustainability assessment and risk management in European banking;safeguarding downside risk in portfolio insurance: navigating Swiss stock market regimes with options, trading signals, and financial products;internal audit: friend or foe of innovation in an organization: case of Czech banking sector;the recruiting process as an attractiveness factor: how do companies manage to position themselves competitively as employers?;developing a framework for city brand-image promotion via social media communication;leveraging multimodal large language models and natural language processing techniques for comprehensive ESG risk score prediction;ChatGPT in higher education: a risk management approach to academic integrity, critical thinking, and workforce readiness;applying text analytics methodology to analyze project reports;and stock market forecasting using machine learning models through volatility-driven trading strategies.

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SmartEDU: A Platform for Generating Education-Support Materials 6

SmartEDU: A Platform for Generating Education-Support Materi...

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6th Experiment at international conference, exp.at 2023

作者： Costa, Maria Joao Matos, Renato Amaro, Hugo Caceiro, Bruno Marques, Alcides Oliveira, Hugo Goncalo Instituto Pedro Nunes Lis Portugal University of Coimbra Cisuc Dei Portugal Mindflow Portugal

ISBN: (纸本)9798350314403

With technology taking a more prevalent role in our daily activities, new opportunities and challenges emerge. New technological tools have been successfully used in the educational context for some time now, facilitating teachers, educators, and trainers in the transmission of knowledge. However, there are still tasks that can take advantage of these developments, as is the case with the creation of multiple-choice questions and slide presentations. Artificial Intelligence, in general, and machine learning and natural language processing, in particular, can be used to automatically generate initial versions of slide decks and of natural language questions. Instead of creating them from scratch, teachers and trainers will only have to perform minor editions, and save time for other tasks. this document presents the platform SmartEDU, which allows users to generate multiple-choice questions and drafts of slide presentations from any text. © 2023 IEEE.

关键词： natural language processing systems

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