F20AA Applied Text Analytics

Dr Neamat El Gayar

Course co-ordinator(s): Dr Neamat El Gayar (Dubai).


This course aims to provide the students with knowledge and skills in applied text analytics focusing on Machine Learning and Natural Language Processing tools.

In particular the course:

- Presents the area of text analytics and provides fundamental tools to extract, represent and analyse information from text sources using machine learning models

- Provides a fundamental understanding of concepts and tools to build effective language aware systems and applications

- Presents basic understanding of deep learning models for Natural Language Processing applications and related research

- Discusses current research advances, business cases and future direction of the field


Detailed Information

Course Description: Link to Official Course Descriptor.

Pre-requisites: none.

Location: Dubai.

Semester: 2.


Following topics will be covered with varying levels of depth:

- Overview on ML models, techniques and use cases & ML project design.

- Language model & text processing principles

- Text classification & visualization

- Text Clustering & topic modelling

- Context-aware text analysis & n-gram model

- Chatbots

- Scaling text analytics

- A deep learning approach to NLP:

o Sequence models (ex: RNN, BRNN, LSTM ) & transfer learning

o Applications in Named Entity Recognition, learning word-embeddings, machine translation, sentiment classification

- Research Directions and Business Cases

Learning Outcomes: Subject Mastery

- Demonstrate understanding of the text analytics process and relevant applications

- Work with text analytic tools to uncover information from text

- Understand challenges related to implementation and scalability

- Understand Deep learning approach to NLP problems and some available tools for implementation

- Demonstrate understanding of some recent advances in the field of NLP & text analytics.

Learning Outcomes: Personal Abilities

-Problem analysis and critical review

-Report writing and presentation skills

- Working in groups

-Use a range of software for ML, text analytics and NLP


Assessment Methods: Due to covid, assessment methods for Academic Year 2021/22 may vary from those noted on the official course descriptor. Please see:
- Maths (F1) Course Weightings 2021/22
- Computer Science (F2) Course Weightings 2021/22
- AMS (F7) Course Weightings 2021/22

SCQF Level: 10.

Credits: 15.