Unit Outline

This unit is designed to give students an insight into a range of natural language processing (NLP) and Generative AI (GenAI) techniques. NLP is a critical step towards effective communication between people and machines. You will learn the basics NLP steps as well as some advanced NLP patterns such as information extraction and text summarisation. This unit includes a number of Artificial Intelligence (AI) areas - classification and clustering, text mining, sentiment analysis, and the use of GenAI for NLP application domains. With the technologies discussed in the lectures, it brings together the state-of-the-art research and practical techniques in NLP, providing students with the knowledge and capacity to conduct NLP research and to develop NLP applications. Students are required to AI, GenAI and NLP tools to explore and specialise their understanding, and also required to use these technologies to develop a system for a NLP application.

Intended Learning Outcomes

As per the Assessment and Results Policy 1.3, your results will reflect your achievement against specified learning outcomes.

On completion of this unit, you will be able to:


1	Describe the principle of NLP processes, methods and the applications of NLP in real applications.
2	Explain the linkage between AI and NLP and be able to adopt suitable AI methods which can be embedded in NLP and text mining approaches.
3	Adopt methodologies, tools, research skills and techniques for the processing, analysing and mining of natural language data.
4	Analyse user needs and incorporate them into the selection, creation, adaption and evaluation of appropriate NLP and text mining methods to support decision making.

Requisites


REQUISITE TYPE	REQUISITES
Pre-requisite	KIT509

Alterations as a result of student feedback

Assignments due dates have been adjusted slightly based on students' feedback.

How will I be Assessed?

For more detailed assessment information please see MyLO.

Assessment schedule


ASSESSMENT TASK #	ASSESSMENT TASK NAME	DATE DUE	WEIGHT	LINKS TO INTENDED LEARNING OUTCOMES
Assessment Task 1:	Project 1 report	Week 6	25 %	LO1, LO2
Assessment Task 2:	Project 2 report	Week 12	25 %	LO2, LO4
Assessment Task 3:	Online test	Week 13	30 %	LO1, LO2, LO3, LO4
Assessment Task 4:	Weekly tutorial tasks	Refer to Assessment Description	20 %	LO1, LO2

Assessment details

Assessment Task 1: Project 1 report

Task Description:

Project 1: NLP

Students will work in a small group to develop a NLP approach given a simple requirement document and a NL dataset. The approach will contain appropriate NLP methods introduced in the lectures. The design and implementation results of the approach will be included in a report.

Task Length:

25 pages

Due Date:

Week 6

Weight:

25 %

CRITERION #

CRITERION

MEASURES INTENDED

LEARNING OUTCOME(S)


1	Explained the problem and background	LO2
2	Justify the selected NLP methods	LO1, LO2
3	Evaluate the designed NLP methods	LO2

Assessment Task 2: Project 2 report

Task Description:

Project 2: text analysis and GAI

The project offers students a chance to implement learned knowledge in an application. Students will form a group to work on an assigned project or a custom project. The final product should show how the idea works for the real application.
Both the developed software product and the project report will be submitted. The project report will include the project objectives, the design rationale, and the implementation specifications.

Task Length:

25 pages

Due Date:

Week 12

Weight:

25 %

CRITERION #

CRITERION

MEASURES INTENDED

LEARNING OUTCOME(S)


1	Explain the motivation and objectives	LO4
2	Justify the selected text mining methods	LO4
3	Evaluate of the designed text mining methods	LO4
4	Justify the conclusion based on the analysis result	LO2, LO4

Assessment Task 3: Online test

Task Description:

Online test

Task Length:

1 hour

Due Date:

Week 13

Weight:

30 %

CRITERION #

CRITERION

MEASURES INTENDED

LEARNING OUTCOME(S)


1	Explain NLP techniques to support decision making	LO1, LO2
2	Analyse and apply techniques to support decision making	LO3, LO4

Assessment Task 4: Weekly tutorial tasks

Task Description:

Tutorial Tasks and Quizzes

Weekly during ten tutorials - students need to complete a practical task or quiz, which is related to what they learned in the previous lecture.

Task Length:

Short programming tasks and quizzes

Due Date:

Refer to Assessment Description

Weight:

20 %

CRITERION #

CRITERION

MEASURES INTENDED

LEARNING OUTCOME(S)


1	Explain the concepts addressed in each given weekly task	LO1, LO2

How your final result is determined

To pass this unit, you need to demonstrate your attainment of each of the Intended Learning Outcomes, achieve a final unit grade of 50% or greater, and pass any hurdle tasks.

Submission of assignments

Where practicable, assignments should be submitted to an assignment submission folder in MYLO. You must submit assignments by the due date or receive a penalty (unless an extension of time has been approved by the Unit Coordinator). Students submitting any assignment in hard copy, or because of a practicum finalisation, must attach a student cover sheet and signed declaration for the submission to be accepted for marking.

Academic integrity

Academic integrity is about acting responsibly, honestly, ethically, and collegially when using, producing, and communicating information with other students and staff members.

In written work, you must correctly reference the work of others to maintain academic integrity. To find out the referencing style for this unit, see the assessment information in the MyLO site, or contact your teaching staff. For more detail about Academic Integrity, see Important Guidelines & Support.

Requests for extensions

If you are unable to submit an assessment task by the due date, you should apply for an extension.

A request for an extension should first be discussed with your Unit Coordinator or teaching support team where possible. A request for an extension must be submitted by the assessment due date, except where you can provide evidence it was not possible to do so. Typically, an application for an extension will be supported by documentary evidence: however, where it is not possible for you to provide evidence please contact your Unit Coordinator.

The Unit Coordinator must notify you of the outcome of an extension request within 3 working days of receiving the request.

Late penalties

Assignments submitted after the deadline will receive a late penalty of 5% of the original available mark for each calendar day (or part day) that the assignment is late. Late submissions will not be accepted more than 10 calendar days after the due date, or after assignments have been returned to other students on a scheduled date, whichever occurs first. Further information on Late Penalties can be found on the Assessments and Results Procedure.

Review of results and appeals

You are entitled to ask for a review of the marking and grading of your assessment task if there is an irregularity in the marking standards or an error in the process for determining the outcome of an assessment. Details on how to request a review of a mark for an assignment are outlined in the Review and Appeal of Academic Decisions Procedure.