Comparative Analysis of Bi-Directional Long Short-Term Memory and BERT Models for Fake News Detection with Explainable AI Using Lime
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Updated
Jan 10, 2024 - Jupyter Notebook
Comparative Analysis of Bi-Directional Long Short-Term Memory and BERT Models for Fake News Detection with Explainable AI Using Lime
Fake News Detection with a Bi-directional LSTM in Keras
Undergraduate Dissertation (University of Malta) 2019/20 - 'Remedi: A Medical Information Extraction System'
Project the performance of three deep learning algorithms namely LSTM, Bi-LSTM, and GRU in performing clickbait news headline classification.
A comparison of vanilla LSTM model and bi-LSTM model for sentiment analysis.
Fake News Classification using bi-LSTM model.
NLP Project - Sentence Classification - Toxicity- Approx 20,000 comments - ranging from 2 to 30 words. Balanced Data Set. 1. Traditional, pre-2010 NLP and ML techniques used. 2. Dense Word Vectors - w2v & Glove, sentence vector created from averaged word vectors, ANN. 3. Glove combined with bi-LSTMs and 2D Convs.
Model Sentiment Analysis Bi-LSTM
Neural sentence embedding using only in-domain sentences for out-of-domain sentence detection in dialog systems
End-to-end review sentiment classification with text preprocessing, Bidirectional Long Short-Term Memory networks and Glove embeddings.
Named Entity Recognition with LSTM, Bi-LSTM, and BERT
Bidirectional LSTM model for detection of amyloid signaling motifs.
This is a special course made with DTU in collaboration with Silvi.ai. The aim is to benchmark DL models in the task of NER for biomedical papers.
Kaggle Competition: Real or Not? NLP with Disaster Tweets.
This repo is devoted to the pracicals of the course Deep Learning (5204DLFV6Y) realised at the Univeristy of Amsterdam, Fall 2020.
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