Durham University
Programme and Module Handbook
This is an archived handbook for a previous year. The current undergraduate and postgraduate handbooks can be viewed using the links above.

Postgraduate Handbook 2019-2020 (archived)

Module COMP42115: Natural Language Analysis

Department: Computer Science

COMP42115: Natural Language Analysis

TypeLevelCreditsAvailabilityModule CapLocation
Tied 4 15 Available in 2019/2020 None. Durham

Tied To

Pre-requisites

  • None

Corequisites

  • None

Excluded Combinations

  • 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

ActivityNumberFrequencyDurationTotal HoursAttendance Monitored
Workshops (a combinationof lectures, laboratories, group work, case studies and discussion) 24 Yes
Preparation and reading 126
Total 150

Summative Assessment

ComponentComponent WeightingElementLength/DurationElement WeightingResit Opportunity
Written Assignment 100% 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.


Students who do not attend monitored activities shown under Teaching Methods and Learning Hours, or who fail to complete the summative or formative assessment(s) specified above, may be subject to the Academic Progress procedures defined in the University's General Regulation V, and may be required to leave the University.