Diffuser et promouvoir la culture en mathématiques et en informatique déployée dans les sciences agronomiques à INRAE et rassembler la communauté des maths-info INRAE.
ECAS – SFdS course
Text data are pervasive and can be leveraged to help solving a wide range of problems. This new source of information coupled with recent advances in text mining have incontestably impacted the industry and academic research. While classical approaches yield reasonable performances on diverse text mining tasks, they make restrictive assumptions incompatible with some properties of natural language. In the last decade, these assumptions have been partly relaxed thanks to important breakthroughs in representation learning and deep learning, enhancing the performance for several tasks.
The school is a first introduction to text mining aimed at a broad audience of practitioners. We’ll present the classical way of pre-processing, encoding and leveraging text data. Then, we’ll introduce recent techniques to learn more meaningful text representations and ways to deal with them using deep neural networks. We’ll stress out the importance of using modern approaches to represent the text through case studies with industrial applications.
Some experience in programming with Python is a plus but not a prerequisite. No prior knowledge of any deep learning framework is required.