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 ?	Enrolment in a thesis unit. INFO4001 or INFO4911 or INFO4991 or INFO4992 or AMME4111 or BMET4111 or CHNG4811 or CIVL4022 or ELEC4712 or COMP4103 or SOFT4103 or DATA4103 or ISYS4103
Prohibitions ?	COMP5046
Assumed knowledge ?	Knowledge of an OO programming language as covered in INFO1113
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 2 April 2024

Details
Criteria

Type	Description	Weight	Due	Length
Supervised exam ?	Final Exam Written exam covering all topics in the course.	50%	Formal exam period	2 hours
Outcomes assessed:
Assignment	N-gram Language Models Implementing and evaluating classical language models	5%	Week 03 Due date: 05 Mar 2024 at 23:59	12 days
Outcomes assessed:
Assignment	Classification Models Implementing and evaluating classification models	5%	Week 05 Due date: 19 Mar 2024 at 23:59	12 days
Outcomes assessed:
Assignment	Sequence Models Implementing and evaluating sequence models	5%	Week 09 Due date: 23 Apr 2024 at 23:59	12 days
Outcomes assessed:
Assignment	Generative Models Implementing and evaluating generative models	5%	Week 11 Due date: 07 May 2024 at 23:59	12 days
Outcomes assessed:
Assignment	Data Annotation Performing and evaluating the data annotation process	5%	Week 13 Due date: 21 May 2024 at 23:59	12 days
Outcomes assessed:
Small continuous assessment	Lab exercises Programming tasks that are completed in the lab	20%	Weekly	2 hours
Outcomes assessed:
Small continuous assessment	Lecture tasks Questions to promote synthesis of concepts	5%	Weekly	n/a
Outcomes assessed:
= hurdle task ?

Assessment summary

Weekly lecture tasks and lab exercises, five short-release assignments, 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.

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.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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 2023 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 2023. 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 Models - Counting	Lecture (2 hr)
Week 01	Introduction and Models - Counting	Computer laboratory (2 hr)
Week 02	Foundation of NLP Systems	Lecture (2 hr)
Week 02	Foundation of NLP Systems	Computer laboratory (2 hr)
Week 03	Models - Non-linear	Lecture (2 hr)
Week 03	Models - Non-linear	Computer laboratory (2 hr)
Week 04	Models - Recurrent	Lecture (2 hr)
Week 04	Models - Recurrent	Computer laboratory (2 hr)
Week 05	Inference - Greedy and Search	Lecture (2 hr)
Week 05	Inference - Greedy and Search	Computer laboratory (2 hr)
Week 06	Inference - Dynamic programming	Lecture (2 hr)
Week 06	Inference - Dynamic programming	Computer laboratory (2 hr)
Week 07	Models - Attention and the Transformer	Lecture (2 hr)
Week 07	Models - Attention and the Transformer	Computer laboratory (2 hr)
Week 08	Models - Encoder-decoder Approaches	Lecture (2 hr)
Week 08	Models - Encoder-decoder Approaches	Computer laboratory (2 hr)
Week 09	Models - Large Language Models	Lecture (2 hr)
Week 09	Models - Large Language Models	Computer laboratory (2 hr)
Week 10	Data - Annotation and crowdsourcing	Lecture (2 hr)
Week 10	Data - Annotation and crowdsourcing	Computer laboratory (2 hr)
Week 11	Training - Unsupervised	Lecture (2 hr)
Week 11	Training - Unsupervised	Computer laboratory (2 hr)
Week 12	Training - Reinforcement Learning	Lecture (2 hr)
Week 12	Training - Reinforcement Learning	Computer laboratory (2 hr)
Week 13	Human-in-the-loop Systems	Lecture (2 hr)
Week 13	Human-in-the-loop Systems	Computer laboratory (2 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 the structure of language
LO2. have developed formal models to express natural language phenomenon
LO3. have developed machine learning and deep learning for solving natural language tasks
LO4. evaluate the performance of natural language processing systems
LO5. implement and debug large NLP systems in a clean and structured manner
LO6. apply machine learning/deep learning methods and information theory principles to modelling language.

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;.
	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. 5.8. Skills in recognising unsuccessful outcomes, sources of error, diagnosis, fault-finding and re-engineering.

Responding to student feedback

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

Creating initial version, following COMP5046 (with which this is co-taught)

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

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

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

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

COMP4446: Natural Language Processing

Semester 1, 2024 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

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

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect