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COMP42115: Natural Language Analysis

It is possible that changes to modules or programmes might need to be made during the academic year, in response to the impact of Covid-19 and/or any further changes in public health advice.

Type Tied
Level 4
Credits 15
Availability Available in 2023/24
Module Cap None.
Location Durham
Department Computer Science

Prerequisites

  • None

Corequisites

  • None

Excluded Combinations 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

ActivityNumberFrequencyDurationTotalMonitored
Lectures91 a week2 hours18Yes
Computer Workshops (max 30 students)41 every two weeks2 hours8Yes
Preparation and reading124 
Total150 

Summative Assessment

Component: Written AssignmentComponent Weighting: 100%
ElementLength / DurationElement WeightingResit Opportunity
Individual written assignment based on the development of a program1500 words100 

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.

More information

If you have a question about Durham's modular degree programmes, please visit our Help page. If you have a question about modular programmes that is not covered by the Help page, or a query about the on-line Postgraduate Module Handbook, please contact us.

Prospective Students: If you have a query about a specific module or degree programme, please Ask Us.

Current Students: Please contact your department.