Course co-ordinator(s): Dr Ioannis Konstas (Edinburgh), Dr Alessandro Suglia (Edinburgh).
Aims:
This course aims to:
- lay the foundations of core concepts in Linguistics;
- present the most common Natural Language Processing (NLP) problems together with appropriate Machine Learning solutions;
- familiarise students with NLP applications in currently active research areas using Machine Learning techniques;
- enable students to build simple NLP applications using commonly used libraries;
- raise critical awareness of the appropriateness of NLP techniques and the relationships between them
Detailed Information
Course Description: Link to Official Course Descriptor.
Pre-requisites: none.
Location: Edinburgh.
Semester: 1.
Syllabus:
Linguistics Foundation - word classes, language models, syntax, semantics
NLP Problems - Part-of-speech tagging, language modelling, syntactic parsing, lexical semantics. Solutions to these problems will employ commonly-used machine learning algorithms, e.g., feature-based discriminative models, dynamic programming, word embeddings, and neural network architectures
NLP Applications - Popular NLP downstream tasks, such as Machine Translation, Machine Reading (Question Answering), and Dialogue Systems.
SCQF Level: 11.
Credits: 15.