Outline

Overview

This unit introduces computational linguistics and the statistical techniques and algorithms used to automatically process natural languages (such as English or Chinese). It will review the core statistics and information theory, and the basic linguistics, required to understand statistical natural language processing (NLP). Statistical NLP is used in a wide range of applications, including information retrieval and extraction; question answering; machine translation; and classifying and clustering of documents. This unit will explore the key challenges of natural language to computational modelling, and the state of the art approaches to the key NLP sub-tasks, including tokenisation, morphological analysis, word sense representation, part-of-speech tagging, named entity recognition and other information extraction, text categorisation, phrase structure parsing and dependency parsing. You will implement many of these sub-tasks in labs and assignments. The unit will also investigate the annotation process that is central to creating training data for statistical NLP systems. You will annotate data as part of completing a real-world NLP task.

Unit details and rules

Academic unit	Computer Science
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	COMP4446
Assumed knowledge ?	Knowledge of an OO programming language
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Jonathan Kummerfeld, jonathan.kummerfeld@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam	Final exam Written exam	60%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Out-of-class quiz	Online quizzes Short online exam-style questions	3%	Multiple weeks	10 minutes	AI allowed
Outcomes assessed:
Practical skill	Word Vectors Implementing and evaluating word vectors	10%	Week 02 Due date: 06 Mar 2026 at 23:00 Closing date: 13 Mar 2026	Code of variable length	AI allowed
Outcomes assessed:
Practical skill	Classification Models Implementing and evaluating classification models	5%	Week 04 Due date: 20 Mar 2026 at 23:59 Closing date: 27 Mar 2026	Code of variable length	AI allowed
Outcomes assessed:
Practical skill	Sequence Models Implementing and evaluating sequence models	10%	Week 07 Due date: 17 Apr 2026 at 23:59 Closing date: 24 Apr 2026	Code of variable length	AI allowed
Outcomes assessed:
Practical skill	Data Annotation Performing and evaluating the data annotation process	10%	Week 09 Due date: 01 May 2026 at 23:59 Closing date: 08 May 2026	Code of variable length	AI allowed
Outcomes assessed:
Written test	Core Concepts Test This test covers core concepts that students must understand in order to pass the unit. It is a hurdle task with a requirement of 80%. There is an opportunity to try again in week 13.	0%	Week 12	50 minutes	AI prohibited
Outcomes assessed:
Practical skill	Lecture tasks Questions to promote synthesis of concepts	2%	Weekly	n/a	AI allowed
Outcomes assessed:
= hurdle task ? = group assignment ?

Assessment summary

Weekly lecture tasks, online quizzes, four assignments, a core concept test (hurdle task), and a final exam.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

This unit limits late submissions, with a maximum of 5 days late over all assignments and a maximum of 2 days late per assignment. There is no penalty within those constraints.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction and Word Vectors	Lecture (2 hr)
Week 01	Introduction and Word Vectors	Workshop (1 hr)
Week 02	Foundation of NLP Systems	Lecture (2 hr)
Week 02	Foundation of NLP Systems	Workshop (1 hr)
Week 03	Models - Non-linear	Lecture (2 hr)
Week 03	Models - Non-linear	Workshop (1 hr)
Week 04	Inference - Greedy and Search	Lecture (2 hr)
Week 04	Inference - Greedy and Search	Workshop (1 hr)
Week 05	Models - Encoder-decoder	Lecture (2 hr)
Week 05	Models - Encoder-decoder	Workshop (1 hr)
Week 06	Models - Transformer	Lecture (2 hr)
Week 06	Models - Transformer	Workshop (1 hr)
Week 07	Models - Large Language Models	Lecture (2 hr)
Week 07	Models - Large Language Models	Workshop (1 hr)
Week 08	Data - Annotation and crowdsourcing	Lecture (2 hr)
Week 08	Data - Annotation and crowdsourcing	Workshop (1 hr)
Week 09	Training - Unsupervised	Lecture (2 hr)
Week 09	Training - Unsupervised	Workshop (1 hr)
Week 10	Models - Agents	Lecture (2 hr)
Week 10	Models - Agents	Workshop (1 hr)
Week 11	Training - Reinforcement Learning	Lecture (2 hr)
Week 11	Training - Reinforcement Learning	Workshop (1 hr)
Week 13	Advanced Topics	Lecture (2 hr)
Week 13	Advanced Topics	Workshop (1 hr)

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

LO1. apply basic linguistic knowledge to identify properties of text
LO2. understand the internal architecture of language models including the purpose of each component
LO3. implement and train machine learning based systems for solving natural language tasks
LO4. evaluate the performance of natural language processing systems
LO5. mplement and debug a large NLP system in a collaborative manner
LO6. annotate data using appropriate quality control methods
LO7. identify ethical concerns in NLP systems and ways to mitigate those issues

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Alignment with Competency standards

Outcomes	Competency standards
	Engineers Australia Curriculum Performance Indicators - 4.3. Proficiency in the engineering design of components, systems and/or processes in accordance with specified and agreed performance criteria. 5.4. Skills in the selection and application of appropriate engineering resources tools and techniques, appreciation of accuracy and limitations;.
	Engineers Australia Curriculum Performance Indicators - 4.3. Proficiency in the engineering design of components, systems and/or processes in accordance with specified and agreed performance criteria. 5.4. Skills in the selection and application of appropriate engineering resources tools and techniques, appreciation of accuracy and limitations;. 5.8. Skills in recognising unsuccessful outcomes, sources of error, diagnosis, fault-finding and re-engineering.
	Engineers Australia Curriculum Performance Indicators - 4.3. Proficiency in the engineering design of components, systems and/or processes in accordance with specified and agreed performance criteria. 5.4. Skills in the selection and application of appropriate engineering resources tools and techniques, appreciation of accuracy and limitations;.

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

In response to student feedback, the large project has been removed and some of its parts have been added to earlier assignments instead.

Additional information

Additional costs

Rather than buying a textbook, students will be expected to pay to query certain API services and cloud computing resources.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Alignment with Competency standards

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Additional costs

Disclaimer

On this page

Useful links

COMP5046: Natural Language Processing

Semester 1, 2026 [Normal evening] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Alignment with Competency standards

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Additional costs

Disclaimer

On this page

Useful links