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During 2021 we will continue to support students who need to study remotely due to the ongoing impacts of COVID-19 and travel restrictions. Make sure you check the location code when selecting a unit outline or choosing your units of study in Sydney Student. Find out more about what these codes mean. Both remote and on-campus locations have the same learning activities and assessments, however teaching staff may vary. More information about face-to-face teaching and assessment arrangements for each unit will be provided on Canvas.

Unit of study_

COMP5046: Natural Language Processing

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.

Details

Academic unit Computer Science
Unit code COMP5046
Unit name Natural Language Processing
Session, year
? 
Semester 1, 2020
Attendance mode Normal day
Location Camperdown/Darlington, Sydney
Credit points 6

Enrolment rules

Prohibitions
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None
Prerequisites
? 
None
Corequisites
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None
Assumed knowledge
? 

Knowledge of an OO programming language

Available to study abroad and exchange students

No

Teaching staff and contact details

Coordinator Caren Soyeon Han, caren.han@sydney.edu.au
Type Description Weight Due Length
Final exam Final exam
50% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO6
Small continuous assessment Lab exercises
10% Multiple weeks n/a
Outcomes assessed: LO1 LO6 LO4 LO3 LO2
Assignment Assignment 1
20% Week 08 n/a
Outcomes assessed: LO2 LO3 LO4 LO5
Assignment Assignment 2
20% Week 14 (STUVAC) n/a
Outcomes assessed: LO2 LO3 LO4 LO5

Detailed information for each assessment can be found on Canvas.

 

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 sydney.edu.au/students/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:

Lab Exercises: No late submission is allowed for any of the assessment during the semester. Assignment 1 and 2: 10% of the available marks per day late; maximum 7 days late (after that: 0).

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

Academic integrity

The Current Student website provides information on academic honesty, academic dishonesty, 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 dishonesty or plagiarism seriously.

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

WK Topic Learning activity Learning outcomes
Week 01 Introduction to natural language processing (2 hr)  
Week 02 Word embedding (word vector for meaning) (2 hr)  
Word embedding (word vector for meaning) (1 hr)  
Week 03 Text classification with machine learning 1 (2 hr)  
Text classification with machine learning 1 (1 hr)  
Week 04 Text classification with machine learning 2 (2 hr)  
Text classification with machine learning 2 (1 hr)  
Week 05 Language fundamental (2 hr)  
Language fundamental (1 hr)  
Week 06 Part of speech tagging (2 hr)  
Part of speech tagging (1 hr)  
Week 07 Dependency parsing (2 hr)  
Dependency parsing (1 hr)  
Week 08 Language model (2 hr)  
Language model (1 hr)  
Week 09 Information extraction 1: named entity recognition (2 hr)  
Information extraction 1: named entity recognition (1 hr)  
Week 10 Information extraction 2: named entity recognition (2 hr)  
Information extraction 2: named entity recognition (1 hr)  
Week 11 Application 1: question and answering (2 hr)  
Application 1: question and answering (1 hr)  
Week 12 Application 2: machine translation (2 hr)  
Application 2: machine translation (1 hr)  
Week 13 Future of NLP and exam review (2 hr)  
Future of NLP and exam review (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 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 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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

Alignment with Competency standards

Outcomes Competency standards
LO1
Stage 1 Competency Standard for Professional Engineer (PG) - EA
1.5 (L2). Discipline context knowledge. (Level 2- Attaining required standard (Bachelor Honours standard AQF9)) Knowledge of contextual factors impacting the engineering discipline.
1.5 (L3). Discipline context knowledge. (Level 3- Exceeding required standard) Knowledge of contextual factors impacting the engineering discipline.
LO2
Stage 1 Competency Standard for Professional Engineer (PG) - EA
1.5 (L3). Discipline context knowledge. (Level 3- Exceeding required standard) Knowledge of contextual factors impacting the engineering discipline.
1.6 (L3). Discipline professional practice knowledge. (Level 3- Exceeding required standard) Understanding of the scope, principles, norms, accountabilities and bounds of contemporary engineering practice in the specific discipline.
LO3
Engineers Australia Curriculum Performance Indicators - EAPI
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;.
LO4
Engineers Australia Curriculum Performance Indicators - EAPI
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.
LO5
Engineers Australia Curriculum Performance Indicators - EAPI
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;.
Stage 1 Competency Standard for Professional Engineer (PG) - EA
1.4 (L3). Discipline research knowledge. (Level 3- Exceeding required standard) Discernment of knowledge development and research directions within the engineering discipline.
1.5 (L3). Discipline context knowledge. (Level 3- Exceeding required standard) Knowledge of contextual factors impacting the engineering discipline.
1.6 (L3). Discipline professional practice knowledge. (Level 3- Exceeding required standard) Understanding of the scope, principles, norms, accountabilities and bounds of contemporary engineering practice in the specific discipline.
LO6
Engineers Australia Curriculum Performance Indicators - EAPI
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.
Stage 1 Competency Standard for Professional Engineer (PG) - EA
1.5 (L3). Discipline context knowledge. (Level 3- Exceeding required standard) Knowledge of contextual factors impacting the engineering discipline.
1.6 (L3). Discipline professional practice knowledge. (Level 3- Exceeding required standard) Understanding of the scope, principles, norms, accountabilities and bounds of contemporary engineering practice in the specific discipline.
2.1 (L3). Complex problem-solving. (Level 3- Exceeding required standard) Application of established engineering methods to complex engineering problem solving.
2.2 (L3). Use of engineering techniques, tools and resources. (Level 3- Exceeding required standard) Techniques, tools and resources.
No changes have been made since this unit was last offered

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