The goal of the course is to make you the best Natural Language researcher and practitioner wherever you go next.
“Deep Learning waves have lapped at the shores of computational linguistics for several years now, but 2015 seems like the year when the full force of the tsunami hit the major Natural Language Processing (NLP) conferences” (Chris Manning).
Natural language processing (NLP) is taking an increasingly dominant part in artificial-intelligence today. Its applications are increasingly appearing in many fields and technologies: web search, advertisement, emails, customer service, language translation, radiology reports, etc. The field of NLP is rich with many underlying tasks – words representations, relation extraction, semantic parsing and more. The machine learning community is thriving with elegant solutions to some of those tasks. Recently, deep learning approaches have obtained very high performance across many different NLP tasks.
In this course we will deep dive to the cutting-edge research in NLP and focus on the latest advances in the application of deep learning in the field. During the course we will cover the theory and practice of neural network models (including: word vector representations, window-based neural networks, recurrent neural networks, long-short-term-memory models, recursive neural networks, and convolutional neural networks).
Course grade will be based on HW assignments and a final course project with lots of programming. Submissions will be done in pairs.
“Deep Learning waves have lapped at the shores of computational linguistics for several years now, but 2015 seems like the year when the full force of the tsunami hit the major Natural Language Processing (NLP) conferences” (Chris Manning).
Natural language processing (NLP) is taking an increasingly dominant part in artificial-intelligence today. Its applications are increasingly appearing in many fields and technologies: web search, advertisement, emails, customer service, language translation, radiology reports, etc. The field of NLP is rich with many underlying tasks – words representations, relation extraction, semantic parsing and more. The machine learning community is thriving with elegant solutions to some of those tasks. Recently, deep learning approaches have obtained very high performance across many different NLP tasks.
In this course we will deep dive to the cutting-edge research in NLP and focus on the latest advances in the application of deep learning in the field. During the course we will cover the theory and practice of neural network models (including: word vector representations, window-based neural networks, recurrent neural networks, long-short-term-memory models, recursive neural networks, and convolutional neural networks).
Course grade will be based on HW assignments and a final course project with lots of programming. Submissions will be done in pairs.