CS918-15 Natural Language Processing

Academic year
24/25
Department
Computer Science
Level
Taught Postgraduate Level
Module leader
Gabriele Pergola
Credit value
15
Module duration
10 weeks
Assessment
Multiple
Study location
University of Warwick main campus, Coventry
Introductory description

Knowledge of the fundamental principles of natural language processing.

Module aims

The aim of the module is to equip students with a fundamental understanding of automated methods for processing linguistic data in textual form (natural language processing) from different sources (newswire, web, social media, academic publications) and associated challenges. The module will also provide students with the skills to analyse textual data and familiarise them with state of the art tools and applications.

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

The module will address core methodologies in natural language processing and related tools and will proceed to examine current applications. The syllabus may cover:

Learning outcomes

By the end of the module, students should be able to:

Indicative reading list

Please see Talis Aspire link for most up to date list.

View reading list on Talis Aspire

Research element

Students need to do some research about features used for sentiment classifier training in Assignment 2

Subject specific skills
Transferable skills

Study time

Type Required
Lectures 20 sessions of 1 hour (13%)
Seminars 8 sessions of 1 hour (5%)
Supervised practical classes 9 sessions of 1 hour (6%)
Private study 113 hours (75%)
Total 150 hours
Private study description

Background reading.
Coursework completion (including programming and report writing).
Revision.

Costs

No further costs have been identified for this module.

You do not need to pass all assessment components to pass the module.

Students can register for this module without taking any assessment.

Assessment group D2
Weighting Study time
Assessed practical coursework 30%

Assessed practical coursework. This assignment is worth more than 3 CATS and is not, therefore, eligible for self-certification.

In-person Examination 70%

CS918 exam


  • Answerbook Pink (12 page)
  • Students may use a calculator
Assessment group R1
Weighting Study time
In-person Examination - Resit 100%

CS918 resit exam


  • Answerbook Pink (12 page)
  • Students may use a calculator
Feedback on assessment

Students will receive written feedback on coursework.

Past exam papers for CS918

Pre-requisites

Self-contained module but it would be helpful to take in conjunction with CS910 and/or CS909.

Courses

This module is Optional for:

  • TCSA-G5PD Postgraduate Taught Computer Science
    • Year 1 of G5PD Computer Science
    • Year 1 of G5PD Computer Science
  • Year 1 of TCSA-G5PA Postgraduate Taught Data Analytics
  • Year 1 of TIMA-L995 Postgraduate Taught Data Visualisation

This module is Core option list A for:

  • Year 1 of TPSS-C803 Postgraduate Taught Behavioural and Data Science

This module is Core option list C for:

  • Year 1 of TPSS-C803 Postgraduate Taught Behavioural and Data Science

This module is Option list A for:

  • Year 1 of TIMS-L990 Postgraduate Big Data and Digital Futures

This module is Option list B for:

  • Year 1 of TIMA-L981 Postgraduate Social Science Research