Note: This is an archived Handbook entry from 2009. Search for this in the current handbook
|Dates & Locations:|| |
This subject is not offered in 2009.
|Time Commitment:||Contact Hours: 24 hours of lectures and 12 hours of workshops; Non-contact time commitment: 84 hours |
Total Time Commitment: Not available
|Prerequisites:||Either an undergraduate degree in Computer Science, Computer Engineering, Software Engineering, Information Technology or related discipline; or at least four Group A Masters subjects|
|Recommended Background Knowledge:||None|
|Non Allowed Subjects:||COMP40011|
|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
|Subject Overview:||Topics covered include the linguistics of words and phrases, part-of-speech tagging, finite-state transducers, chart parsing and chunk parsing, hidden Markov models, n-gram language models, spelling and grammar checking, collocation analysis, word-sense disambiguation, text retrieval, information extraction, and machine translation. Programming work will be undertaken in the Python language, and will use NLTK, the Natural Language Toolkit (nltk.org).|
|Objectives:||On completion of this subject students will be: familiar with the foundations of symbolic and statistical natural language processing; familiar with key concepts in language description and analysis; able to develop and evaluate computational models of language; and familiar with a variety of human language technologies.|
|Assessment:||Four projects, expected to take about 48 hours, during semester (50%) and a 2-hour end of semester written examination (50%). To pass the subject, students must obtain at least 50% overall, 25/50 in the projects, and 25/50 in the written examination.|
|Breadth Options:|| |
This subject is not available as a breadth subject.
|Fees Information:||Subject EFTSL, Level, Discipline & Census Date|
|Generic Skills:||On successful completion, students should: |
|Notes:||Credit may not be gained for both 433-460 Human Language Technology and 433-660 Human Language Technology.|
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