Natural Language Processing (COMP90042)
Graduate courseworkPoints: 12.5On Campus (Parkville)
Overview
| Availability | Semester 1 |
|---|---|
| Fees | Look up fees |
AIMS
Much of the world's knowledge is stored in the form of text, and accordingly, understanding and harnessing knowledge from text are key challenges. In this subject, students will learn computational methods for working with text, in the form of natural language understanding, and language generation. Students will develop an understanding of the main algorithms used in natural language processing, for use in a diverse range of applications including machine translation, text mining, sentiment analysis, and question answering. The programming language used is Python.
INDICATIVE CONTENT
Topics covered may include:
- Text classification and unsupervised topic discovery
- Vector space models for natural language semantics
- Structured prediction for tagging
- Syntax models for parsing of sentences and documents
- N-gram language modelling
- Automatic translation, and multilingual methods
- Relation extraction and coreference resolution
Intended learning outcomes
On completion of this subject the student is expected to:
- Identify basic challenges associated with the computational modelling of natural language
- Understand and articulate the mathematical and/or algorithmic basis of common techniques used in natural language processing
- Implement relevant techniques and/or interface with existing libraries
- Carry out end-to-end research experiments, including evaluation with text corpora as well as presentation and interpretation of results
- Critically analyse and assess text-processing systems and communicate criticisms constructively
Generic skills
On completing this subject, students should have the following skills:
- Formulate and implement algorithmic solutions to computational problems, with reference to the research literature
- Apply a systems approach to complex problems, and design for operational efficiency
- Design, implement and test programs for small and medium size problems in the Python programming language
Last updated: 4 March 2025
Eligibility and requirements
Prerequisites
One of
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| COMP30027 | Machine Learning | Semester 1 (On Campus - Parkville) |
12.5 |
| COMP90049 | Introduction to Machine Learning |
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
|
12.5 |
| COMP90051 | Statistical Machine Learning |
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
|
12.5 |
| No longer available |
OR
Admission into or selection of one of the following:
- 100pt Program course entry point in the MC-IT Master of Information Technology
- 150pt Program course entry point in the MC-IT Master of Information Technology
AND
- Admission into or selection of one of the following:
- Artificial Intelligence (100pt) specialisation (formal) in the MC-IT Master of Information Technology
- Artificial Intelligence (150pt) specialisation (formal) in the MC-IT Master of Information Technology
- Distributed Computing (100pt) specialisation (formal) in the MC-IT Master of Information Technology
- Distributed Computing (150pt) specialisation (formal) in the MC-IT Master of Information Technology
- Computing (100pt) specialisation (formal) in the MC-IT Master of Information Technology
- Computing (150pt) specialisation (formal) in the MC-IT Master of Information Technology
Corequisites
None
Non-allowed subjects
433-460 Human Language Technology
433-467 Text and Document Management
433-660 Human Language Technology
433-667 Text and Document Management
433-476 Text and Document Management
Inherent requirements (core participation requirements)
The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.
Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home
Last updated: 4 March 2025
Assessment
| Description | Timing | Percentage |
|---|---|---|
2 short programming assignments. Assignment 1: due Week 5; weight 9%; duration 8-10 hours. Assignment 2: due Week 6; weight 8%; duration 7-10hours.
| From Week 5 to Week 6 | 17% |
A group-based (group of 2-3) research project essay working from Week 6 to 12 with a Week 12 submission.
| From Week 6 to Week 12 | 35% |
1 peer review assignment of other student groups' research project essays.
| Week 12 | 8% |
One examination.
| During the examination period | 40% |
Additional details
Hurdle Requirement:
Hurdle: 50% overall 30/60 for continuous assessment 20/40 for final exam
Last updated: 4 March 2025
Dates & times
- Semester 1
Principal coordinator Jey Han Lau Mode of delivery On Campus (Parkville) Contact hours 36 hours, comprising of two 1-hour lectures and one 1-hour tutorial per week Total time commitment 200 hours Teaching period 3 March 2025 to 1 June 2025 Last self-enrol date 14 March 2025 Census date 31 March 2025 Last date to withdraw without fail 9 May 2025 Assessment period ends 27 June 2025 Semester 1 contact information
Jey Han Lau
Time commitment details
200 hours
What do these dates mean
Visit this webpage to find out about these key dates, including how they impact on:
- Your tuition fees, academic transcript and statements.
- And for Commonwealth Supported students, your:
- Student Learning Entitlement. This applies to all students enrolled in a Commonwealth Supported Place (CSP).
Subjects withdrawn after the census date (including up to the ‘last day to withdraw without fail’) count toward the Student Learning Entitlement.
Last updated: 4 March 2025
Further information
- Texts
- Subject notes
LEARNING AND TEACHING METHOD
The subject comprises a weekly 2 hour lecture followed by a 1 hour laboratory exercise. Weekly readings are assigned from relevant textbooks and the research literature, and weekly laboratory exercises are assigned. Additionally, a significant amount of project work is assigned.
INDICATIVE KEY LEARNING RESOURCES
At the beginning of the semester, the coordinator will post a list of readings taken from relevant textbooks as well as research literature and research monographs. An indicative source of relevant material is the textbook Speech and Language Processing by Dan Jurafsky and James H. Martin (2008).
CAREERS / INDUSTRY LINKS
A growing sector of the IT industry is concerned with leveraging the information that is locked up in semi-structured text data on the web. Large scale analysis and exploitation of this information depends on graduates with a solid grounding in natural language processing and text retrieval algorithms, and experience with implementing systems that are informed by the research literature.
- Related Handbook entries
This subject contributes to the following:
Type Name Course Master of Science (Computer Science) Course Ph.D.- Engineering Course Master of Data Science Course Doctor of Philosophy - Engineering Course Master of Philosophy - Engineering Specialisation (formal) Software - Available through the Community Access Program
About the Community Access Program (CAP)
This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.
Please note Single Subject Studies via Community Access Program is not available to student visa holders or applicants
Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.
Additional information for this subject
Subject coordinator approval required
- Available to Study Abroad and/or Study Exchange Students
Last updated: 4 March 2025