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Irony-Sarcasm-Detection-Task
The detection of irony and sarcasm is one of the most insidious challenges in the field of Natural Language Processing. Over the years, several techniques have been studied to analyze these rhetorical figures, trying to identify the elements that discriminate, in a significant way, what is sarcastic or ironic from what is not. Within this study, some models that are state of the art are analyzed. As far as Machine Learning is concerned, the most discriminating features such as part of speech, pragmatic particles and sentiment are studied. Subsequently, these models are optimized, comparing Bayesian optimization techniques and random search. Once, the best hyperparameters are identified, ensemble methods such as Bayesian Model Averaging (BMA) are exploited. In relation to Deep Learning, two main models are analyzed: DeepMoji, developed by MIT, and a model called Transformer Based, which exploits the generalization power of Roberta Transformer. As soon as these models are compared, the main goal is to identify a new system able to better capture the two rhetorical figures. To this end, two models composed of attention mechanisms are proposed, exploiting the principle of Transfer Learning, using Bert Tweet Model and DeepMoji Model as feature extractors. After identifying the various architectures, an ensemble method is applied on the set of approaches proposed, in order to identify the best combination of algorithms that can achieve satisfactory results. Frameworks used: Pytorch, TF 2.0, Scikit Learn, Scikit-Optimize, TransformersCalFram
Calibration Framework for Machine Learning and Deep LearningText-Mining---Toxic-comment
Several models were developed in order to solve the text classification task on toxic comments. Several models have been created trying to give an innovative approach to solve the unbalanced class problem.The-Maze-Problem
Implementation of traditional,hybrid algorithms and Reinforcement LearningStatistical-Modeling
How to deal with statistical analysisSocial-Media-Analytics
Twitter post analysis and community detectionObject-and-Sound-detection
Application of Deep Learning and Machine Learning techniques for traffic sing detection and word trigger recognitionLove Open Source and this site? Check out how you can help us