Outline

Overview

This unit introduces the computational methods, methodological concepts, and theoretical ideas that underpin modern methods for processing natural language (such as English) using computers. NLP is used in a wide range of applications, including information retrieval and extraction; question answering; machine translation; code generation; dialogue; and classifying and clustering of documents. To achieve these, NLP systems perform a range of tasks using mathematical representations of language. In the context of these applications, the unit will explore common modelling methods, including heuristics, linear models, and neural networks, up to and including large language models (LLMs). The unit covers key ideas relevant to NLP from machine learning, statistics, linguistics, and data science. Students will implement NLP systems and evaluation metrics in labs and assignments. The unit will also investigate the annotation process for creating evaluation data for NLP systems. Students 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

Type	Description	Weight	Due	Length
Supervised exam ?	Final exam Written exam	50%	Formal exam period	2 hours
Outcomes assessed:
Small continuous assessment	Lab exercises Questions to check mastery of contents	5%	Multiple weeks	n/a
Outcomes assessed:
Assignment	Assignment 1 Implementation and Documentation	20%	Week 04 Due date: 19 Mar 2023 at 23:59	n/a
Outcomes assessed:
Assignment	Assignment 2 Implementation and Documentation	20%	Week 12 Due date: 15 May 2023 at 23:59	n/a
Outcomes assessed:
Small continuous assessment	Lecture tasks Questions to promote synthesis of concepts	5%	Weekly	n/a
Outcomes assessed:

Assessment summary

Weekly lecture tasks and lab exercises, two assignments, and a final exam.

Assessment criteria

The University awards common result grades, set out in the听听(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	听
Distinction	75 - 84	听
Credit	65 - 74	听
Pass	50 - 64	听
Fail	0 - 49	When you don鈥檛 meet the learning outcomes of the unit to a satisfactory standard.

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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.

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:

Standard University Late Penalty Policy (5% per day late, then 0 if 10 or more days late)

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 .

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 .

Outside assessment tasks, generative AI tools may be used to support your learning. The 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听simple extension.鈥� 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鈥檛 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听special consideration or special arrangements.

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

Using AI responsibly

Co-created with students,听听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.

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction to natural language processing	Lecture (2 hr)
Week 01	Introductions and set up	Computer laboratory (1 hr)
Week 02	Word representations	Lecture (2 hr)
Week 02	Word representations	Computer laboratory (1 hr)
Week 03	Text classification with machine learning 1	Lecture (2 hr)
Week 03	Text classification with machine learning 1	Computer laboratory (1 hr)
Week 04	Text classification with machine learning 2	Lecture (2 hr)
Week 04	Text classification with machine learning 2	Computer laboratory (1 hr)
Week 05	Fundamentals of language	Lecture (2 hr)
Week 05	Fundamentals of language	Computer laboratory (1 hr)
Week 06	Part of speech tagging	Lecture (2 hr)
Week 06	Part of speech tagging	Computer laboratory (1 hr)
Week 07	Dependency parsing	Lecture (2 hr)
Week 07	Dependency parsing	Computer laboratory (1 hr)
Week 08	Language models	Lecture (2 hr)
Week 08	Language models	Computer laboratory (1 hr)
Week 10	Named Entity Recognition and Coreference Resolution	Lecture (2 hr)
Week 10	Named Entity Recognition and Coreference Resolution	Computer laboratory (1 hr)
Week 11	Question Answering	Lecture (2 hr)
Week 11	Question Answering	Computer laboratory (1 hr)
Week 12	Machine translation	Lecture (2 hr)
Week 12	Machine translation	Computer laboratory (1 hr)
Week 13	Future of NLP and exam review	Lecture (2 hr)
Week 13	Future of NLP and exam review	Computer laboratory (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 identifying 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 爆料王 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鈥檚 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

Important: the 爆料王 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 .

爆料王

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

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

爆料王

COMP4446: Natural Language Processing

Semester 1, 2023 [Normal evening] - Remote

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

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