Module Specification

Natural Language Processing

Module Specification

Natural Language Processing



During this module, students will learn about natural language processing (NLP) and how to reason with text computationally. NLP applications, such as text analysis, computational social science, the digital humanities, and computational journalism, draw on both linguistics and computer science. The focus in this module will be on algorithms used in NLP, including part-of-speech, machine translation, text classification, as well as the linguistic phenomena they attempt to model. Historical and modern algorithms built upon sophisticated deep learning algorithms will be looked at. Students will consider word semantics and embeddings, neural architectures for sequence modelling, and open domain question answering. Through a series of hands-on assignments, students will also learn the technical skills required to build and debug NLP algorithms, and test their strengths and weaknesses.


Mode(s) of Study Code CATS Credits ECTS Credits Framework HECoS code
Full-time blended
Part-time blended
15 7 FHEQ - L7 natural language processing (100961)

Prerequisites and Co-requisites

  • Corequisite: Deep Learning
  • Prerequisite: Applied Machine Learning

Learning Outcomes

Code Attributes developed Outcomes
LO1 Knowledge and Understanding Comprehensive knowledge of NLP and text classification models, including sentiment analysis, and those using discriminative, generative and other approaches.
LO2 Intellectual Skills Critically evaluate research experiments for testing the performance of NLP algorithms.
LO3 Technical/Practical Skills Construct sequence modelling architectures such as LSTM and Transformer models for advanced NLP tasks.
LO4 Technical/Practical Skills Design and train NLP models, analysing performance using perplexity, smoothing and relevant methods and techniques.
LO5 Technical/Practical Skills Interpret complex linguistic data through advanced text processing, segmentation, and normalisation techniques.
LO6 Professional/Transferable Skills Communicate natural language generation techniques, considering ethical implications in artificial intelligence applications.

Content Structure

Week Topic
Week 1 Introductory lecture
Introduces the module, outlining its relevance to the field and connections to other topics. It provides an overview of the content structure, key references, and assessment details.
Week 2 Text Processing
Students explore the technical groundwork of text processing, learning about word and sentence segmentation, stemming, and normalization. This chapter instils an understanding of how intricate text patterns are computed and the importance of precise modification of linguistic data for further analysis.
Week 3 N-Gram Language Models
Focusing on language models, this chapter introduces n-grams and their application in computing the probability of word sequences. Students decipher how perplexity measures model performance and learn about techniques for sampling and smoothing n-grams for proficient text prediction.
Week 4 Classification Paradigms in NLP
Students investigate the realm of text classification, including sentiment analysis and other linguistically nuanced applicative models. The chapter covers the evaluation of classifying outcomes and outlines discriminative and generative approaches for effective classification tailored to specific contexts.
Week 5 Word Semantics
The chapter dives into vector semantics, and various embedding models, delving into the statistical methods and neural approaches that undergird the representation of words and phrases in a computational medium, alongside their context-related subtleties.
Week 6 Part of Speech Tagging
Here, students master the identification of grammatical components and name entities within text, sharpening their skills in unravelling sentence structures and meanings. The focus extends to the practical execution of tagging and named entity recognition employing neural methods.
Week 7 Neural Architectures
This chapter elaborates on the architectures for text sequence modelling, including LSTM networks and variants. Students gain expertise in neural sequence decoding, understanding the critical role of time dependencies and the foundational mechanisms of attention.
Week 8 Machine Translation Models
Students gain practical insights into machine translation, learning about the encoder-decoder framework and sequence-to-sequence models. Issues of language diversity and the algorithms vital for capturing nuances in the translation process are investigated.
Week 9 Transformers in Machine Translation
The chapter engages students with the advent of transformer models in machine translation and their novel approaches to encoding and decoding textual information. The implications of transfer learning and the use of pre-trained models are critically assessed.
Week 10 Parsing Sentences
Exploring the intricacies of parsing sentences, students will uncover complex grammatical structures employing modern span-based neural parsing techniques. Emphasis is placed on the extraction of structured information from text, encompassing relations, events, and templates.
Week 11 Artificial Intelligence Generation
Concluding with natural language generation, students are exposed to the components of NLG systems, the process of formalization, and ethical considerations. This chapter ensures learners can interpret and evaluate the output from NLG models and grasp their societal impact.

Student Workload

The methods of teaching and learning for this module are based on the School's Technical 15 teaching system, consisting of the following activities.

Activity Total hours
Introductory lecture 1.50
Concept learning (knowledge graph) 18.00
AI formative assessment 9.00
Case Study Review 9.00
Workshop/Lab Sessions 13.50
Individual or group assignments 18.00
Independent reading, exploration and practice 51.00
Summative assessment 30.00
Total: 150.00

Teaching and Learning Methods

Activity Description
Introductory lecture

This is the first weekly session, dedicated to providing a comprehensive introduction to the module. The module leader will present an overview of the subject, elucidating its importance within various digital engineering professions and its interrelation with other modules. Students will need no preparation ahead of attending this session.

The module leader will provide a structured breakdown of the content to be covered in the subsequent 9 sessions. Students will also receive an outline of the essential reference materials, alongside suggestions for supplementary reading. The format and criteria for the summative assessment will be delineated, followed by a dedicated period for questions and answers.

A recording of the session will be available to facilitate async engagement for any other student who missed the class, also offering an opportunity to review the content again.

Concept learning (knowledge graph)

Our institution's approach to teaching is primarily based on flipped learning. Ahead of each weekly session (Workshop/Lab), students will be required to study the essential concepts that are used in the coming session so they are familiar with the theories and ideas related to that session. The study material will be in the form of written content, illustrations, pre-recorded lectures and tutorials, and other forms of content provided through the AGS.

This content is self-navigated by the students, accommodating different learning styles and schedules, allowing students to watch or listen to them at their own pace and review them as needed.

AI formative assessment

Once each concept of the theory is studied, students will be prompted to engage in formative assessment with instant AI feedback. They include multiple-choice questions, socratic questions and answers, written questions, role-play and other AI-assisted practice scenarios.

The purpose of this automated formative assessment is to provide students with immediate feedback on their understanding of module material and highlight any areas that need support or further study. They are also used to track student progress, boost motivation and promote accountability.

Case Study Review

In this learning activity, students explore recent real-world case studies relevant to their course topic. The case studies will have been selected and curated by the module leader to represent up-to-date examples. They guide students through key details, contextual factors, and outcomes. This approach enhances students' understanding of current industry trends, challenges, and solutions, preparing them for real-world scenarios they may encounter in their future careers.

The learning experienced will be augmented by AI (virtual private tutor) allowing the students to critically engage with the content and discuss the case studies.

Workshop/Lab Sessions

Those studying in the blended learning mode will attend these 9 weekly classes (in person or remotely) during weeks 2 to 10. These sessions will complement the theory already studied during the preceding week (in our flipped-learning model), with discussions, analysis, practice or experience . They will be interactive and participatory, rather than one-way lectures. There will also be an opportunity for Q&A in every session. Depending on the nature of the content, challenges and learning activities will be pre-designed to apply flipped learning. They may include hands-on project work, group discussions or debates, roleplay, simulation, case studies, presentations, and other learning activities and opportunities. These workshops present an opportunity to apply critical thinking and problem-solving skills. They also encourage collaboration and foster a sense of community among students.

Individual or group assignments

Each Workshop/Lab session will be followed by an assignment. Assignments are used to reinforce learning and encourage independent thinking and problem-solving. They help the students identify the gaps in their understanding of the subject and provide them with an opportunity to apply what they have learned in a practical setting.

Assignments can be individual or group-based (teams of 2 to 4). They can take many forms, including essays, presentations, or projects. When they are group-based, teams will be randomly picked by AGS, in order to promote broader teamwork practice. Assignment files will be uploaded to AGS by the students ahead of the next weekly session. Feedback will be provided on each submitted assignment.

Independent reading, exploration and practice

This activity challenges students to engage with the reference material and independently explore and analyse academic literature related to the course topic. Students are expected to select relevant sources, practice critical reading skills, and where applicable technical skills, and synthesise information from multiple references. This is an opportunity to enhance research abilities, critical thinking, and self-directed learning skills while broadening and deepening subject knowledge.

Summative assessment

Summative assessments are used to evaluate student learning at the end of a module. These assessments can take many forms, including exams, papers, or presentations. Instructors can use summative assessments to measure whether students have achieved the learning outcomes for the module and provide them with a sense of their overall progress. Summative assessments can also be used to evaluate the effectiveness of the teaching methods used in the module.


Assessment Patterns

Weighting Format Outcomes assessed
40% Invigilated Exam
This is a time-limited and closed-book exam with a mix of multiple-choice and analytical written questions that students undertake during the summative assessment period as scheduled under the School’s remote invigilation conditions to ensure quality and academic integrity.

The exam enables the students to demonstrate their successful attainment of the module learning outcomes, primarily related to knowledge and understanding, and secondarily related to Professional/Transferable Skills.

The analytical written questions will consist of problem questions representing issues and dilemmas students are likely to encounter in professional life and students have to synthesise and apply what they have learnt on the module in order to produce sound and reasoned judgements with respect to the problem.

To enable the students to practice and prepare, various formative assessment activies, including quizzes and a AI-augmented assignments and mock exams are built into the module. Additionally, throughout the course, students will regularly receive feedback on their knowledge and assignments from AI as well as peers and staff to indicate how to improve future work and how to give constructive feedback to others.
K LO1
I LO2
P LO6
60% Technical Analysis and Solution Assessment
This assessment requires students to develop a solution to a complex problem within a simulated domain, followed by a detailed analysis and reflection on their design and its theoretical underpinnings. The aim is to assess students' abilities to design practical solutions, critically analyse their work, and articulate their understanding of the technical and theoretical aspects of the module.
K LO1
I LO2
T LO3
T LO4
T LO5
P LO6

References/Indicative Reading List

Importance ISBN Description
Supplementary Reading Jurafsky, Dan, and James H. Martin. "Speech and Language Processing", Pearson, (2008)

Programmes Linked to This Module

There are no programme modules to display.

Module Approval

Stage Version Date of approval Authority Chair Revalidation
Compliance 1.0 Academic Board Dr Paresh Kathrani
Pre-Teaching 1.0 Director of Education Dr Paresh Kathrani
Note: The information detailed within this record is accurate at the time of publishing and may be subject to change.
Module Spec: Natural Language Processing ()