MS3S33 - Natural Language Processing 01 Sep 2021 - 31 Aug 2027 | Version 1
Associated Module Information
| Module Code: | MS3S33 | ||
|---|---|---|---|
| Module Title: | Natural Language Processing | ||
| Faculty: | Faculty of Computing, Engineering and Science | ||
| Faculty Group: | Computing and Mathematical Sciences | ||
| Faculty Sub Group: | Mathematical Sciences | ||
| Module Leader: | Penny Holborn, Ian Fitzell | ||
| Module Team: | Moizzah Asif, Angelica Pachon, Rebecca Peters, Samuel Jobbins | ||
| First Intended Intake: | SEP 2021 | Final Year of Intake: | 2024 |
| Date Closed: | |||
| Credit Value: | 20 | Credit Level: | 6 |
| Language: | English | ||
| Percentage of Module Taught in Welsh: | 0 | ||
| Equivalent Module: | |||
| HECOS codes: | |||
| HECOS Code Weighting: | |||
Document Version Information
| Version | 1 |
|---|---|
| Valid From | 01 Sep 2021 |
| Valid To | 31 Aug 2027 |
Module Aims
To analyse unstructured data in the form of text including basic descriptive tasks, sentiment analysis and topic analysis to identify trends and patterns.
To apply a range of natural language algorithms to critically evaluate patterns and relationships from text.
Content Summary
Introduction to the concepts of text mining including natural language processing techniques and text representation, which are the foundation for all kinds of text-mining applications.
Case studies on text classification, topic modelling, sentiment analysis and social media mining.
Pre-processing of text and initial bag of words approaches will be explored. Along with advanced topis to including Language Models, BERT and Word Embeddings.
Learning and Teaching Methods
| Activity Type | Hours |
|---|---|
| Lecture | 10 |
| Practical classes and workshops | 10 |
| Supervised time in studio/workshop | 6 |
| Work based learning | 74 |
| Directed Study | 28 |
| Formative Assessment - Independent | 72 |
| Total Hours Selected | 200 |
Learning Outcomes
| # | Learning Outcome |
|---|---|
| LO1 | To understand the methods for analysing unstructured data. |
| LO2 | To utilise and critically evaluate a variety of NLP algorithms to assess and interpret real-world complex unstructured data. |
Module Requisites
N/A
Assessment Criteria
| Assessment Category | Assessment Type | Description | Duration | Word Count | Weight (%) | Best of? | Pass Mark |
|---|---|---|---|---|---|---|---|
| Portfolio | Portfolio 1 | Portfolio | 0 | 6000 | 100 | No | 40 |
Assessment Matrix
| Assessment Type | Learning Outcomes | ||
|---|---|---|---|
| LO1 | LO2 | ||
| Portfolio 1 | ✔ | ✔ | |