Postgraduate Programme and Module Handbook 2024-2025
Module COMP42115: Natural Language Analysis
Department: Computer Science
COMP42115: Natural Language Analysis
| Type | Tied | Level | 4 | Credits | 15 | Availability | Available in 2024/2025 | Module Cap | None. |
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| Tied to | G5K709 |
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Prerequisites
- None
Corequisites
- None
Excluded Combination of Modules
- None
Aims
- To introduce students to cutting-edge techniques for automated analysis of textual data and their applications
Content
- Preparation of textual data for machine learning
- Advanced machine learning techniques for natural language analysis
- Application of natural language analysis techniques within business analytics e.g. sentiment analysis, social media analysis
Learning Outcomes
Subject-specific Knowledge:
- Upon successful completion of the module, the students will:
- Have a critical appreciation of how natural language texts can be effectively represented for machine learning
- Have an advanced understanding of automated natural language analysis through machine learning
- Understand how natural language analysis can be applied effectively within business analytics
Subject-specific Skills:
- Upon successful completion of the module, the students will:
- Be able to prepare natural language texts for machine learning
- Be able to train a machine learning application based on real textual data
Key Skills:
- Effective written communication
- Planning, organising and time-management
- Problem solving and analysis
- Reflecting and synthesising from experience
Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module
- Learning outcomes are met through classroom-based workshops, supported by online resources. The workshops consist of a combination of taught input, group work, case studies, discussion and computing labs. Online resources provide preparatory material for the workshops – typically consisting of directed reading and video content.
- The summative assessment is an individual written assignment based on the development of a program to analyse a real natural language data set. This is designed to test students’ skills in problem identification, their theoretical understanding, and their ability to analyse the situation in order to categorise the potential solutions.
Teaching Methods and Learning Hours
| Activity | Number | Frequency | Duration | Total/Hours | |
|---|---|---|---|---|---|
| Lectures | 9 | 1 a week | 2 hours | 18 | ■ |
| Computer Workshops (max 30 students) | 4 | 1 every two weeks | 2 hours | 8 | ■ |
| Preparation and reading | 124 | ||||
| Total | 150 |
Summative Assessment
| Component: Written Assignment | Component Weighting: 100% | ||
|---|---|---|---|
| Element | Length / duration | Element Weighting | Resit Opportunity |
| Individual written assignment based on the development of a program | 1500 words | 100% | |
Formative Assessment:
A range of formative assessment methods will be used, including case study based exercises, group presentations and group discussions, simulation exercises and business games designed to prepare students for the summative business report. Oral and written feedback will be provided on an individual and/or group basis as appropriate.
■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University