Back to ListNatural Language Proccessing and Analysis
Level
Intermediate, Niche, Algorithmic, Methodological
This topic concerns machine learning for Natural Language Processing (NLP).
Learning outcomes
Content /Knowledge
Students should be able to:
- Understand/describe the basics of language modelling, tokenization, normalisation, stemming, lemmatization and Parts-of-Speech (POS) tagging.
- Compare between Bag-of-Words, N-Grams, TF-IDF and learned word embedding-based representations of text.
- Understand/describe common algorithms for question answering, text classification, text/document summarization, sentiment analysis, sentence similarity estimation, speech recognition and neural machine translation.
Methodologicalskills
Students should be able to:
- Analyse and develop (in C/C++, MATLAB or Python) the taught NLP algorithms, by practically applying their gained knowledge in a systematic manner.
- Evaluate the accuracy of NLP algorithm implementations on suitable datasets, by employing common and appropriate task-specific metrics.
Transferrable/Application
Students should be able to:
- Work effectively with others in an interdisciplinary and/or international team.
- Design and manage individual projects.
- Clearly and succinctly communicate their ideas to technical audiences.