Objective
The objective of the practice module is twofold:
Firstly, to expose participants to real world problems so that they may practice the use of the skills they have learned during the component courses in a holistic manner.
Secondly, to enable participants to demonstrate their proficiency across all of the skills that they have learned in the course modules and hence obtain a grade at the Graduate Certificate Level.
Intended Audience
This practice module is targeted at participants who wish to complete the certification process for the Graduate Certificate in “Practical Language Processing”.
Prerequisites
Participants must have successfully obtained a competent score (or have been exempted from) the four component courses for the Graduate Certificate in Practical Language Processing as listed in the introduction to the Graduate Certificate page.
Components
There are two parts to the Practice Module.
- Practice Project: Participants will need to undertake one or more projects to gain practical experience and demonstrate their understanding and mastery of the skills taught in the four component courses. The practice project will require each participant to expend an estimated 10 man-days of effort. These days are not expected to be contiguous and may stretch over many weeks. These projects may be conducted by individual participants or in teams depending on the nature of the project requirements.
- Examination: Each participant is required to sit for an examination on a stipulated date and time.
- The overall grade for the participant will be based on the Practice project and Examination.
Typical example of projects to be undertaken
- Problem description:
A restaurant would like to have an additional channel other than emails, phone calls, and web forms to interact with their customers, and be available 7 days a week, 24 hours a day. They want this channel to be able to converse with the customers, identify and handle what the customers want to do, such as answering common customer queries, guiding the customers through reservation service, and receiving and forwarding customer feedbacks to respective staffs. This requires a conversational UI that can interpret the customer’s input correctly and produce appropriate response based on the situation, as well as back ends that perform sentiment analysis, question answering, topic discovery, etc. using appropriate language processing techniques.
Deliverables and success criteria:
- The overall system is to solve a business problem using language data
- The system must have a conversational user interface, either speech or text.
- The system should perform at least two text mining tasks, e.g. text classification, text clustering, topic modelling, information extraction, collocation detection, sequence labelling etc.
- The system must have a component performing sentiment mining.
- At least one of the above task must be implemented using deep neural network.