CE807-7-SP-CO:
Text Analytics
2022/23
Computer Science and Electronic Engineering (School of)
Colchester Campus
Spring
Postgraduate: Level 7
Current
Monday 16 January 2023
Friday 24 March 2023
15
12 January 2023
Requisites for this module
(none)
(none)
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MSC G51512 Big Data and Text Analytics
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 module assumes a reasonable programming background and is not suitable for students without prior programming experience.
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.
Outline Syllabus:
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.
This module does not appear to have a published bibliography for this year.
Assessment items, weightings and deadlines
| Coursework / exam |
Description |
Deadline |
Coursework weighting |
| Coursework |
Assignment 1 - Interim Practical Text Analytics and Report |
|
25% |
| Coursework |
Assignment 2 - Final Practical Text Analytics and Report |
|
75% |
Additional coursework information
This assignment involves developing a text categorization system--e.g., for sentiment analysis of Twitter data. The assessment is going to be based in part on the code, in part on the report. In the new coursework-only version of the course, in the report students will also be asked to answer theoretical questions. Assignment 1 is to be handed out in week 20 and submitted to FASer in week 22.
This assignment involves the development of a system for, e.g., named entity resolution, or disambiguation to Wikipedia of query logs. The assessment is based in part on the code produced, in part on report, which, in the new version of the module, will also require the students to answer some theoretical questions. Assignment 2 will be handed out in week 23, to be submitted to FASer in week 25.
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
Reassessment
Module supervisor and teaching staff
Dr Ravi Shekhar, email: r.shekhar@essex.ac.uk.
Dr Ravi Shekhar, Dr Michael Walton
School Office, email: csee-schooloffice (non-Essex users should add @essex.ac.uk to create full e-mail address), Telephone 01206 872770
Yes
No
Yes
Dr Colin Johnson
University of Nottingham
Dr MARJORY CRISTIANY Da COSTA ABREU
Sheffield Hallam University
Senior Lecturer
Available via Moodle
Of 140 hours, 20 (14.3%) hours available to students:
120 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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