Natural Language Processing (COMP90042)
Graduate courseworkPoints: 12.5On Campus (Parkville)
For information about the University’s phased return to campus and in-person activity in Winter and Semester 2, please refer to the on-campus subjects page.
Please refer to the LMS for up-to-date subject information, including assessment and participation requirements, for subjects being offered in 2020.
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
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: 3 November 2022
Eligibility and requirements
Prerequisites
One of the following:
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| COMP30018 | Knowledge Technologies | No longer available | |
| COMP90049 | Introduction to Machine Learning |
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
| COMP30027 | Machine Learning | Semester 1 (On Campus - Parkville) |
12.5 |
OR Admission into MC-IT Master of Technology, 100 or 150 point program in Distributed Computing or Computing
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: 3 November 2022
Assessment
Due to the impact of COVID-19, assessment may differ from that published in the Handbook. Students are reminded to check the subject assessment requirements published in the subject outline on the LMS
| Description | Timing | Percentage |
|---|---|---|
3 short programming assignments.
| From Week 3 to Week 10 | 20% |
A final, open-ended research project, due at the end of the semester .
| From Week 11 to Week 12 | 30% |
One examination.
| End of semester | 50% |
Additional details
Hurdle Requirement: To pass the subject, students must obtain at least:
- 50% overall
- 25/50 in the continuous assessment
- 25/50 in the end-of-semester written examination
Intended Learning Outcomes (ILOs) 1 and 2 are addressed in the lectures, workshops, and exam; ILOs 3 and 4 are addressed in the assignments and project.
Last updated: 3 November 2022
Dates & times
- Semester 1
Principal coordinator Jey Han Lau Mode of delivery On Campus (Parkville) Contact hours 36 hours, comprising of one 2-hour lecture and one 1-hour workshop per week Total time commitment 200 hours Teaching period 2 March 2020 to 7 June 2020 Last self-enrol date 13 March 2020 Census date 30 April 2020 Last date to withdraw without fail 5 June 2020 Assessment period ends 3 July 2020 Semester 1 contact information
Jey Han Lau
Time commitment details
200 hours
Last updated: 3 November 2022
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 Master of Data Science Course Ph.D.- Engineering Course Doctor of Philosophy - Engineering Course Master of Philosophy - Engineering Specialisation (formal) Computing Specialisation (formal) Distributed Computing Specialisation (formal) Software Major Computer Science - 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: 3 November 2022