Natural Language Engineering
Computer Science and Electronic Engineering (School of)
Undergraduate: Level 6
Thursday 08 October 2020
Friday 18 December 2020
04 October 2018
Requisites for this module
BSC L310 Sociology with Data Science,
BSC L311 Sociology with Data Science (including Year Abroad),
BSC L312 Sociology with Data Science (including Placement Year),
BSC L313 Sociology with Data Science (including foundation Year)
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 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
- 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
||Assignment 1 - Practical exercise 1
||Assignment 2 - Practical exercise 2
||120 minutes during Summer (Main Period) (Main)
Module supervisor and teaching staff
Mr Michael Walton, email: firstname.lastname@example.org.
School Office, email: csee-schooloffice (non-Essex users should add @essex.ac.uk to create full e-mail address), Telephone 01206 872770
Dr Iain Phillips
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).
* Please note: due to differing publication schedules, items marked with an asterisk (*) base their information upon the previous academic year.
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