Module overview
This module gives students an introduction to natural language processing (NLP) algorithms and an understanding of how to implement NLP applications.
Linked modules
prerequisites: COMP6245 or COMP6246
Aims and Objectives
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 Practical Skills
Having successfully completed this module you will be able to:
- Implement NLP algorithms and techniques
- Process text corpora ready for application of NLP algorithms and techniques
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- Show an appreciation of the landscape of tools used for NLP within application areas
- Describe and critically appraise the different subareas of NLP
Syllabus
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
- Text Chunking
- Dependency Parsing
- Word Senses and WordNet
Sequence Processing with Recurrent Neural Networks
- Recurrent Neural Networks
- Sequence Processing for NLP Applications
- Managing Context using LSTM’s and GRU’s
Information Extraction
- Named Entity Recognition
- Relation Extraction
- Temporal, Event and Location Extraction
Applications of NLP (example topics below)
- Statistical Machine Translation
- Text Summarization
- Question Answering
Learning and Teaching
Teaching and learning methods
Lectures and formative laboratories.
| Type | Hours |
|---|---|
| Wider reading or practice | 45 |
| Revision | 10 |
| Specialist Laboratory | 8 |
| Completion of assessment task | 45 |
| Lecture | 24 |
| Preparation for scheduled sessions | 18 |
| Total study time | 150 |
Resources & Reading list
Internet Resources
Speech and Language Processing, 3rd ed. draft (online preferred).
Textbooks
Jurafsky and Martin (2009). Speech and Language Processing. Prentice Hall.
Steven Bird, Ewan Klein & Edward Loper (2009). Natural Language Processing with Python. O'Reilly Media.
Aurélien Géron (2017). Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O′Reilly Media.
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Examination | 75% |
| Coursework | 25% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Examination | 100% |
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.
| Method | Percentage contribution |
|---|---|
| Examination | 100% |
Repeat Information
Repeat type: Internal & External