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Semi-Supervised Machine Learning & Deep Learning Models in Crisis-Related Informativeness Classification

Alessandro Rennola

Semi-Supervised Machine Learning & Deep Learning Models in Crisis-Related Informativeness Classification.

Rel. Elena Maria Baralis, Cornelia Caragea. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2019

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Abstract:

This study examines the impact of several state-of-the-art Machine Learning and Deep Learning techniques in the context of semi-supervised disaster-related twitter mining. The goal is to create a model able to successfully classify informative tweets in the context of (natural) disasters by using several approaches: - Machine Learning: Naive Bayes and Support Vector Machines - Deep Learning: Convolutional Neural Networks, Bidirectional Long Short Term Memory mechanisms. Subsequently, the supervised models are extended to assess the impact of semi-supervised techniques (self-training for NB, SVM, CNN; Virtual Adversarial Loss Function for BiLSTM).

Relators: Elena Maria Baralis, Cornelia Caragea
Academic year: 2019/20
Publication type: Electronic
Number of Pages: 96
Subjects:
Corso di laurea: Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering)
Classe di laurea: New organization > Master science > LM-32 - COMPUTER SYSTEMS ENGINEERING
Ente in cotutela: UNIVERSITY OF ILLINOIS AT CHICAGO (STATI UNITI D'AMERICA)
Aziende collaboratrici: UNSPECIFIED
URI: http://webthesis.biblio.polito.it/id/eprint/12435
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