Computer Science and Electronic Engineering (School of)
Postgraduate: Level 7
Sunday 17 January 2021
Friday 26 March 2021
08 May 2019
Requisites for this module
MSC G51512 Big Data and Text Analytics,
MSC G41224 Artificial Intelligence and its Applications
This module will provide an understanding of text analytics and its applications. Students will study state of the art matters for supervised and unsupervised text mining. Methods include rule based traditional machine learning as well as deep neural networks.
The aim of this module is to provide students with an understanding of basic and advanced methods of text analytics and its applications. Students will learn about state of the art methods for unsupervised and supervised text mining including text preprocessing, structured data extraction, clustering of documents and classification of documents using different techniques. The methods taught include rule-based approaches, traditional machine learning techniques as well as modern Deep Neural Networks.
After completing this module, students will be expected to be able to:
1. Have knowledge about methods for text preprocessing.
2. Understand and use techniques for structured data extraction.
3. Understand and use various techniques for statistical text analysis.
4. Apply text analysis methods on data extracted from the web such as social media , websites and others.
5. Be empowered to independently develop systems for text analytics.
1. Text preprocessing techniques
2. Structured data extraction (such as entities, records)
3. Statistical methods for text clustering (unsupervised learning)
4. Statistical methods for text classification (supervised learning)
5. Deep Learning for text analysis (supervised and unsupervised)
2 hours of lectures per week, 2 hours of laboratory time per week.
- Coelho, Luis Pedro; Richert, Willi. (2015) Building machine learning systems with Python, Birmingham: Packt Publishing.
- Manning, Christopher D.; Raghavan, Prabhakar; Schütze, Hinrich. (2008) Introduction to information retrieval, New York: Cambridge University Press.
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 - Interim Practical Text Analytics and Report
||Assignment 2 - Final Practical Text Analytics and Report
Module supervisor and teaching staff
Prof Ansgar Scherp, email: email@example.com.
School Office, e-mail csee-schooloffice (non-Essex users should add @essex.ac.uk to create full e-mail address), Telephone 01206 872770.
Dr Robert Mark Stevenson
University of Sheffield
Available via Moodle
Of 40 hours, 20 (50%) hours available to students:
20 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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