Note: This is an archived Handbook entry from 2015.
|Dates & Locations:|| |
This subject has the following teaching availabilities in 2015:Semester 1, Parkville - Taught on campus.
Timetable can be viewed here. For information about these dates, click here.
|Time Commitment:||Contact Hours: 36 hours, comprising of one 2-hour lecture and one 1-hour workshop per week |
Total Time Commitment:
One of the following:
Study Period Commencement:
Semester 1, Semester 2
Semester 1, Semester 2
|Recommended Background Knowledge:|| |
|Non Allowed Subjects:||
433-460 Human Language Technology
|Core Participation Requirements:||
For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Student Support and Engagement Policy, academic requirements for this subject are articulated in the Subject Overview, Learning Outcomes, Assessment and Generic Skills sections of this entry.
It is University policy to take all reasonable steps to minimise the impact of disability upon academic study, and reasonable adjustments will be made to enhance a student's participation in the University's programs. Students who feel their disability may impact on meeting the requirements of this subject are encouraged to discuss this matter with a Faculty Student Adviser and Student Equity and Disability Support: http://services.unimelb.edu.au/disability
CoordinatorAssoc Prof Steven Bird
The aims for this subject is for students to develop an understanding of the main algorithms used in natural language processing and text retrieval, for use in a diverse range of applications including search engines, cross-language information retrieval, machine translation, text mining, question answering, summarisation, and grammar correction. Topics to be covered include text normalisation, sentence boundary detection, part-of-speech tagging, n-gram language modelling, and text classification. The programming language used is Python.
Topics covered will include:
NTENDED LEARNING OUTCOMES (ILO)
On completion of this subject the student is expected to:
Hurdle requirement: To pass the subject, students must obtain at least:
Intended Learning Outcomes (ILOs) 1 and 2 are addressed in the lectures, workshops, and exam; ILOs 3 and 4 are addressed in the project work and oral presentation.
|Breadth Options:|| |
This subject is not available as a breadth subject.
|Fees Information:||Subject EFTSL, Level, Discipline & Census Date|
On completing this subject, students should have the following skills:
LEARNING AND TEACHING METHODS
The subject comprises a weekly 2 hour lecture followed by a 1 hour laboratory exercise. Weekly readings are assigned from 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 from the research literature and research monographs which will form the basis of the intellectual content of the subject. An indicative monograph is Statistical Machine Translation, by Philipp Koehn (2010).
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.
Master of Information Technology |
Master of Philosophy - Engineering
Master of Science (Computer Science)
Master of Software Systems Engineering
B-ENG Software Engineering stream |
MIT Computing Specialisation
MIT Distributed Computing Specialisation
Master of Engineering (Software)
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