Natural Language Engineering

The details
Computer Science and Electronic Engineering (School of)
Colchester Campus
Postgraduate: Level 7
Thursday 03 October 2024
Friday 13 December 2024
21 March 2024


Requisites for this module



Key module for

MSC G51512 Big Data and Text Analytics,
MSC G456N3 Computing

Module description

Students are not permitted to undertake this module if they have done the equivalent module of CE314 at UG level on a CSEE undergraduate course. This is because it cannot count towards your course credits. Please contact the CSEE School Office for specific advice on your module choices. 

As humans we are adept in understanding the meaning of texts and conversations. We can also perform tasks such as summarize a set of documents to focus on key information, answer questions based on a text, and when bilingual, translate a text from one language into fluent text in another language. Natural Language Engineering (NLE) aims to create computer programs that perform language tasks with similar proficiency. This course provides a strong foundation to understand the fundamental problems in NLE and also equips students with the practical skills to build small-scale NLE systems. Students are introduced to three core ideas of NLE: a) gaining an understanding the core elements of language--- the structure and grammar of words, sentences and full documents, and how NLE problems are related to defining and learning such structures, b) identify the computational complexity that naturally exists in language tasks and the unique problems that humans easily solve but are incredibly hard for computers to do, and c) gain expertise in developing intelligent computing techniques which can overcome these challenges. The module assumes a reasonable programming background and is not suitable for students without prior programming experience.

Module aims

The aim of this module is:

  • To introduce key ideas and techniques used in the design and implementation of natural language engineering applications. We will primarily cover statistical methods, and will look at the use of such methods in applications.

Module learning outcomes

By the end of this module, students will be expected to be able to:

  1. Describe and formalize how language problems can be solved computationally.

  2. Understand and implement techniques for language modelling, speech tagging, and syntactic parsing.

  3. Understand and implement techniques for computational semantics and discourse processing.

  4. Understand, implement, and use algorithms such as Viterbi decoding, and basic supervised classification.

  5. Understand how NLE techniques can be used to design and implement applications such as text summarization, sentiment analysis, and writing quality prediction.

Module information

Outline Syllabus

  • Language models

  • Topic classification

  • Part-of-speech tagging

  • Syntactic parsing

  • Lexical semantics

  • Discourse processing

  • NLE applications such as text summarization, sentiment analysis, and identifying writing quality

Learning and teaching methods

This module will be delivered via:

  • Lectures.
  • Labs.


  • Jurafsky, Dan; Martin, James H. (c2009) Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition, Upper Saddle River, N.J.: Pearson Prentice Hall. vol. Prentice Hall series in artificial intelligence
  • Speech and Language Processing: draft of 3rd edition, https://web.stanford.edu/~jurafsky/slp3/
  • Bird, Steven; Klein, Ewan; Loper, Edward. (c2009) Natural language processing with Python, Beijing: O'Reilly.

The above list is indicative of the essential reading for the course. The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students. Further reading can be obtained from this module's reading list.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Coursework weighting
Coursework   Assignment 1 - Practical Exercise 1     33.33% 
Coursework   Assignment 2 - Practical Exercise 2     66.67% 
Exam  Main exam: In-Person, Open Book (Restricted), 120 minutes during Early Exams 
Exam  Reassessment Main exam: In-Person, Open Book (Restricted), 120 minutes during September (Reassessment Period) 

Exam format definitions

  • Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
  • In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
  • In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
  • In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary, for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.

Your department will provide further guidance before your exams.

Overall assessment

Coursework Exam
30% 70%


Coursework Exam
30% 70%
Module supervisor and teaching staff
Dr Yunfei Long, email: yl20051@essex.ac.uk.
Dr Yunfei Long
School Office, email: csee-schooloffice (non-Essex users should add @essex.ac.uk to create full e-mail address), Telephone 01206 872770



External examiner

Sheffield Hallam University
Senior Lecturer
Available via Moodle
Of 535 hours, 0 (0%) hours available to students:
535 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).


Further information

* Please note: due to differing publication schedules, items marked with an asterisk (*) base their information upon the previous academic year.

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