Results 71 to 80 of about 1,103 (177)
Unpacking Sarcasm: A Contextual and Transformer-Based Approach for Improved Detection
ComputersSarcasm detection is a crucial task in natural language processing (NLP), particularly in sentiment analysis and opinion mining, where sarcasm can distort sentiment interpretation.
Parul Dubey +2 more
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Artificial Intelligence vs. Human: Decoding Text Authenticity with Transformers
Future InternetThis paper presents a comprehensive study on detecting AI-generated text using transformer models. Our research extends the existing RODICA dataset to create the Enhanced RODICA for Human-Authored and AI-Generated Text (ERH) dataset.
Daniela Gifu, Covaci Silviu-Vasile
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Enhancing Text Classification Through Grammar-Based Feature Engineering and Learning Models
InformationText classification remains a challenging task in natural language processing (NLP) due to linguistic complexity and data imbalance. This study proposes a hybrid approach that integrates grammar-based feature engineering with deep learning and ...
Alaa Mohasseb +2 more
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Chaos and FractalsIn the rapidly evolving landscape of natural language processing (NLP) and artificial intelligence, recent years have witnessed significant advancements, particularly in text-based question-answering (QA) systems. The Stanford Question Answering Dataset
Cem Özkurt
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DistilBERT-Based Hybrid Architecture for Phishing URL Detection
IEEE AccessPhishing continues to be a major and rapidly evolving challenge in cybersecurity. By disguising malicious links as legitimate ones, attackers trick users into revealing sensitive information such as login credentials and financial details.
Ulku Ozmen, Esra Odabas Yildirim
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Activity Classification in E-Commerce Product Reviews Using Deep Learning and Transformer Models
InformaticsExisting research on e-commerce product reviews has primarily focused on analysing consumers’ opinions, emotions, sentiments and associated star ratings. Whilst these approaches provide insights into consumers’ perceptions of products, they offer limited
Tinashe Wamambo +4 more
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Fine-Tuning distilBERT for Enhanced Sentiment Classification
Journal of Big Data and ComputingThis research examines the fine-tuning of the DistilBERT model for sentiment classification using the IMDB dataset of 50,000 movie reviews. Sentiment analysis is vital in natural language processing (NLP), providing insights into emotions and opinions within textual data.
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Exploring Variability in Fine-Tuned Models for Text Classification with DistilBERT
CoRRThis study evaluates fine-tuning strategies for text classification using the DistilBERT model, specifically the distilbert-base-uncased-finetuned-sst-2-english variant. Through structured experiments, we examine the influence of hyperparameters such as learning rate, batch size, and epochs on accuracy, F1-score, and loss.
Giuliano Lorenzoni +3 more
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INTENT RECOGNITION USING DISTILBERT AND LANGUAGE MODELS
Intent classification in Natural Language Processing involves identifying the intention of the user based on their input/interaction with an interface. This can be in a natural usage setting (voice assistants) or an interaction between users, customer service personnel, or agents (in a large organization). This paper aims to study the problem of intent
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Fine-tuning DistilBERT for new classification
This thesis presents a study on the development and optimization of a natural language processing (NLP) model for the automatic classification of news according to their thematic categories (politics, sports, entertainment, etc.). The work focuses on the application and evaluation of machine learning techniques based on artificial neural networks, with
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