3-day Tutorial (Tue, Wed, Thu): Using Sentiment Analysis as a Case Study for Introducing Modern NLP Concepts
|Frequency||Every day, until 13th Feb 2018|
|Date||Thursday 1 - Saturday 3 January 1970|
|Time||5pm (Tuesday 13 February) - 6:20pm (Thursday 15 February)|
|Presenter||Dr Felipe Bravo-Marquez|
Sentiment analysis is the task of automatically classifying text into sentiment categories such as positive and negative. Until 2014, state-of-the-art solutions to this problem relied on shallow learning schemes based on hand-crafted features and linear machine learning models. Deep neural networks architectures had became very popular in the computer vision community due to its success for detecting objects (“cat”, “bicycles”) regardless of its position in the image. These approaches have also been recently adopted for many natural language processing (NLP) tasks, including sentiment analysis, with successful results. In this tutorial, we use sentiment analysis as a case study for introducing modern neural network architectures for NLP, including word embeddings, convolutional neural networks, and recurrent neural networks.
No previous linguistic knowledge is required. Basic understanding of mathematical concepts such as functions, matrices, and derivatives may be helpful but is not essential.
Attention: This is a 3-day tutorial, the concepts covered in each day depend on what was taught in previous days. This is not the same talk repeated three times.
About the Speaker:
Felipe Bravo-Marquez is a research fellow in the Machine Learning Group at the University of Waikato, New Zealand. He received his PhD degree from the University of Waikato. Previously, he received two engineering degrees in the fields of computer science and industrial engineering, and a masters degree in computer science, all from the University of Chile. He worked for three years as a research engineer at Yahoo! Labs Latin America.
His main areas of interest are: data mining, natural language processing, information retrieval, and sentiment analysis.