My interests are in the broad intersection of natural language processing and machine learning. I work mainly in the areas of text generation, parsing on its varieties and representation learning and analysis. I use various tools, including large language models, neural networks, (multi)linear algebra and probabilistic grammars.
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Natural language processing is an application area in computer science, heavily supported by the industry with new applications emerging on a constant basis. The goal of this course is to give a different angle and look into natural language processing. We will explore basic concepts in computer science, machine learning, and statistics that make natural language processing such a rich area of research. You will learn how to use generic methods for application to specific problems you need to address in order to make use of natural language. As such, we will take a method-oriented view of NLP instead of an application-oriented one.
Topics we will discuss include: basic probability and statistics used in NLP, structured prediction with log-linear models, Bayesian inference, finite state transducers, context-free grammars and other constructs, latent-variable modeling, basic concepts in learning theory.
Hopefully, after taking the class, when using a generic NLP tool such as a part-of-speech tagger or a syntactic parser, you will be able to hypothesize how the tool generally works under the hood and why. This class can also assist you later in research in natural language processing, should you choose to pursue a PhD degree in the area.
scohen [strudel] inf.ed.ac.uk
10 Crichton Street
Informatics Forum 4.26
Edinburgh EH8 9AB
United Kingdom
Phone: +44 (0) 131 650 6542
