CE887-7-AU-CO:
Natural Language Engineering

The details
2019/20
Computer Science and Electronic Engineering (School of)
Colchester Campus
Autumn
Postgraduate: Level 7
Current
Thursday 03 October 2019
Saturday 14 December 2019
15
04 October 2018

 

Requisites for this module
(none)
(none)
(none)
CE314

 

(none)

Key module for

MSC G51512 Big Data and Text Analytics

Module description

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.

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

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.

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

Lectures and Labs/Classes

Bibliography

  • 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 Weighting
Coursework Assignment 1 - Practical Exercise 1 15/11/2019 33.33%
Coursework Assignment 2 - Practical Exercise 2 13/12/2019 66.67%
Exam 120 minutes during Summer (Main Period) (Main)

Overall assessment

Coursework Exam
30% 70%

Reassessment

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

 

Availability
Yes
No
No

External examiner

Dr Robert Mark Stevenson
Senior Lecturer
Resources
Available via Moodle
Of 37 hours, 25 (67.6%) 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).

 

Further information

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