Module Details

Module Code: COMP9066
Title: Natural Language Processing
Long Title: Natural Language Processing
NFQ Level: Expert
Valid From: Semester 1 - 2020/21 ( September 2020 )
Duration: 1 Semester
Credits: 5
Field of Study: 4811 - Computer Science
Module Delivered in: 1 programme(s)
Module Description: Natural language processing (NLP) is a set of statistical and machine learning techniques applied to the analysis and synthesis of natural language and speech. This module will provide learners with a comprehensive introduction to the theory underpinning NLP and will also equip learners with the knowledge to implement and apply NLP algorithms and techniques to real-world problems such as sentiment analysis.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Apply and evaluate a language modelling technique such as n-grams to a natural language processing problem.
LO2 Compare and contrast the use of parsing techniques for context-free grammar problems.
LO3 Develop and evaluate a document classification model using machine learning techniques.
LO4 Implement a machine translation model for real-world data and assess its performance.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements

This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
 
Indicative Content
Language Modelling
Introduction to natural language processing and language models. N-gram modelling, The Markov assumption and maximum likelihood estimation. Evaluating language models, perplexity, generalization, smoothing techniques and dealing with unknown words. Hidden Markov models and part-of-speech tagging.
Parsing for NLP
Context free grammar. Syntactic parsing. Structural, attachment and coordination ambiguity. Handling structural ambiguities using the CKY algorithm. Statistical parsing, probabilistic context free grammars for disambiguation. Learning PCFG rule probabilities. Dependency Parsing. Dependency grammars and typed dependency structure.
Machine Learning
Document classification using machine learning techniques such as naive bayes (mutli-nomial and bernoulli models), support vector machines, logistic regression and neural networks (embedding dense word vectors).
Machine Translation (MT)
Introduction to linguistic knowledge. Rule-based MT (transfer-based MT and inter-lingual MT. Statistical MT (word and phrase-based translation). Neural MT and vector-based representations. MT evaluation metrics (WER, BLEU and TER).
Module Content & Assessment
Assessment Breakdown%
Coursework100.00%

Assessments

Coursework
Assessment Type Project % of Total Mark 50
Timing Week 8 Learning Outcomes 1,2,3
Assessment Description
Build a language model and use it in a given natural language processing application such as text generation. Produce a report that critically analyses the performance of the model.
Assessment Type Project % of Total Mark 50
Timing Week 12 Learning Outcomes 3,4
Assessment Description
Implement a machine model such as a neural model with vector-based representations for tasks of Machine Translation or Question answering. Assess the performance of the model using standard techniques such as BLEU or WER.
No End of Module Formal Examination
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.

The University reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Delivers the concepts and theories underpinning the learning outcomes. Every Week 2.00 2
Lab Contact Application of learning to case studies and project work. Every Week 2.00 2
Independent Learning Non Contact Student undertakes independent study. The student reads recommended papers and practices implementation. Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Delivers the concepts and theories underpinning the learning outcomes. Every Week 2.00 2
Lab Contact Application of learning to case studies and project work. Every Week 2.00 2
Independent Learning Non Contact Student undertakes independent study. Student reads recommended papers and practices implementation. Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
 
Module Resources
Recommended Book Resources
  • N. Hardeniya J. Perkins, D. Chopra, N. Joshi, I. Mathur. (2016), Natural Language Processing: Python and NLTK, 1st. Packt Publishing, [ISBN: 9781787285101].
  • L. Hobson. H. Cole, H. Hannes. (2017), Natural Language Processing in Action: Understanding, analyzing, and generating text with Python, 1st. Manning Publications, [ISBN: 9781617294631].
Supplementary Book Resources
  • C. Manning. (1999), Foundations of Statistical Natural Language Processing, 4th. MIT Press, [ISBN: 9780262133609].
Recommended Article/Paper Resources
Supplementary Article/Paper Resources
This module does not have any other resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_KARIN_9 Master of Science in Artificial Intelligence 2 Elective