Natural Language Engineering
Computer Science and Electronic Engineering (School of)
Postgraduate: Level 7
Thursday 05 October 2023
Friday 15 December 2023
01 March 2023
Requisites for this module
MSC G51512 Big Data and Text Analytics
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.
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.
After completing 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.
NLE applications such as text summarization, sentiment analysis, and identifying writing quality
Lectures and Labs/Classes
This module does not appear to have a published bibliography for this year.
Assessment items, weightings and deadlines
|Coursework / exam
||Assignment 1 - Practical Exercise 1
||Assignment 2 - Practical Exercise 2
||Main exam: In-Person, Open Book (Restricted), 120 minutes during Early Exams
||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.
Module supervisor and teaching staff
Dr Yunfei Long, email: email@example.com.
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
Dr MARJORY CRISTIANY Da COSTA ABREU
Sheffield Hallam University
Available via Moodle
Of 44 hours, 32 (72.7%) hours available to students:
12 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s), module, or event type.
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