Hobart, Launceston
Introduction
This unit is designed to give students an insight into a range of natural language processing (NLP) techniques. NLP is a critical step towards effective communication between people and machines. You will learn the basics NLP steps as well as some advanced NLP patterns such as information extraction and text summarisation. This unit includes a number of Artificial Intelligence (AI) areas - classification and clustering, text mining, sentiment analysis, and the use of web technology for NLP application domains (Semantic Web, information retrieval, chatbot).
With the technologies discussed in the lectures, it brings together the state-of-the-art research and practical techniques in NLP, providing students with the knowledge and capacity to conduct NLP research and to develop NLP applications. Students are required to use web application programming interfaces (APIs) and text mining tools to explore and specialise their understanding, and also required to use these technologies to develop a system for a NLP web application.
Summary 2021
| Unit name | Artificial Intelligence and Natural Language |
|---|---|
| Unit code | KIT719 |
| Credit points | 12.5 |
| Faculty/School | College of Sciences and Engineering School of Information and Communication Technology |
| Discipline | Information & Communication Technology |
| Coordinator | Quan Bai |
| Level | Postgraduate |
| Available as student elective? | No |
| Breadth Unit? | No |
Availability
Note
Please check that your computer meets the minimum System Requirements if you are attending via Distance/Off-Campus.
Units are offered in attending mode unless otherwise indicated (that is attendance is required at the campus identified). A unit identified as offered by distance, that is there is no requirement for attendance, is identified with a nominal enrolment campus. A unit offered to both attending students and by distance from the same campus is identified as having both modes of study.
Special approval is required for enrolment into TNE Program units.
TNE Program units special approval requirements.
* The Final WW Date is the final date from which you can withdraw from the unit without academic penalty, however you will still incur a financial liability (see withdrawal dates explained for more information).
Learning Outcomes
1 | Be able to describe the principle of NLP processes, methods and the applications of NLP in real applications. related AI methods in NLP. |
2 | Be able to explain the linkage between AI and NLP. Be able to adopt suitable AI methods which can be embedded in NLP and text mining approaches. |
3 | Adopt methodologies, tools, research skills and techniques for the processing, analysing and mining of natural language data. |
4 | Analyse user needs and incorporate them into the selection, creation, adaption and evaluation of appropriate NLP and text mining methods to support decision making. |
Fees
Teaching
| Teaching Pattern | 1 x 60 min lecture weekly, 1 x 120 min workshop weekly |
|---|---|
| Assessment | AT1 - Project proposal (20%) AT2 - Project (25%) AT3 - Tutorial tasks (15%) AT4 - 2-hour exam (40%) |
| Timetable | View the lecture timetable | View the full unit timetable |
Textbooks
| Required | None |
|---|
The University reserves the right to amend or remove courses and unit availabilities, as appropriate.