This course covers the fundamental and recent understandings of coreference resolution strategies. Coreference resolution is the act of identifying all expressions. NLP tasks such as text summarization, answering, and information extraction. In this course, we will cover the linguistic concepts relevant to corference resolution and study some classical coreference resolution algorithms. So we want to go over the automatic coreference resolution systems and discuss the methods for evaluating the outcome of these automated systems.

Project seminar dealing with "questions processing" conceived broadly. In the first part, we will look at different NLP tasks related to questions (question generation, question answering, question classification, etc.) and discuss existing datasets and resources. Then we'll settle on projects to carry out together.

This class is the graduate-level introduction to computational linguistics, a first-year class in the MSc Cognitive Systems. The purpose of this class is to introduce the important concepts, models and methods used in natural language processing (NLP). After the successful completion of this course, students should be able to (i) read and understand the scientific literature in the area of computational linguistics and (ii) start implementing their own NLP projects.

We will cover the following topics:

statistical models of language
part of speech tagging (HMMs)
syntactic parsing (PCFGs, others?)
semantics
machine translation
speech processing
classification
and more

Foundation of linguistics for CogSys students.