Natural Language Processing

Learning Outcomes

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

• Key concepts, tools and approaches for handling textual data
• Algorithms commonly used for NLP problems such as information extraction, machine translation, text summarization and question answering
• The underlying algorithmic and linguistic basis for NLP techniques

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

• Be able to describe and discuss the different subareas of NLP
• Understand the potential and limitations of NLP techniques within application areas

Subject Specific Practical Skills

Having successfully completed this module you will be able to:

• Be able to implement NLP algorithms and techniques
• Be able to process text corpora ready for application of NLP algorithms and techniques

Working with Text Corpora

• Text Normalization
• Regular Expressions
• Evaluation Metrics and Linguistic Resources

Vector Semantics and Embeddings

• Lexical and Vector Semantics
• TF-IDF
• Word2Vec

Language Modelling and Parts of Speech Tagging

• Language Modelling
• Parts of Speech Tagging

Syntactic and Semantic Parsing

• Syntactic parsing
• Dependency Parsing
• Word Senses and WordNet

Sequence Processing with Recurrent Neural Networks

• Recurrent Neural Networks
• Sequence Processing for NLP Applications
• LSTM, GRU and Transformer models

Large Language Models

• Masked Language Models
• Prompting and Instruction Tuning

Information Extraction

• Named Entity Recognition
• Relation Extraction
• Temporal, Event and Location Extraction

Applications of NLP (example topics below)

• Statistical Machine Translation
• Semantic Role Labelling
• Question Answering

Teaching and learning methods

Lectures and formative laboratories.

Resources & Reading list

Internet Resources

Speech and Language Processing, 3rd ed. draft (online preferred).

Textbooks

S.Bird, E.Klein & E.Loper (2009). Natural Language Processing with Python. O'Reilly.

Aurelien Geron (2017). Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O′Reilly.

Jurafsky and Martin (2009). Speech and Language Processing. Prentice Hall.

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.

Repeat Information

Repeat type: Internal & External