Studying online

There are now 2 possible online modes for units:

Units with modes Online timetabled and Online flexible are available for any student to self-enrol and study online.

Click on an offering mode for more details.

Unit Overview


Natural Language has been and will remain as the most preferred way to store and transfer knowledge. More than 80% of electronic data in modern societies are generated and stored in textual format. How to process unstructured text to extract useful insights and support actionable decision making and discover the hidden treasure of collective intelligence is of enormous value. In this unit, we start with traditional text processing techniques using Regular Expressions and discuss the needs of text processing and normalisation. We then introduce fundamental pipelines of natural language processing (NLP), including part-of-speech tagging and various ways of sentence parsing, with the aim of introducing traditional text feature collection techniques for higher-level tasks such as sentiment or document classification. Building on the understanding of the pros and cons of feature-based NLP pipeline approaches, the unit moves onto the modern approach of deep learning for NLP, focusing on word vector representation, neural language models, and recurrent neural networks for NLP. The unit situates the techniques around major NLP tasks, including information extraction, sentiment detection, dialogue systems and machine translation.

6 points
(see Timetable)
Semester 1UWA (Perth)Face to face

Students are able to (1) apply pre-processing techniques for textual data preparation; (2) build pipelines for core NLP tasks; (3) critically analyse different language models; (4) explain how vector representations of words can be obtained; (5) evaluate performance of NLP solutions, both traditional and neural; and (6) undertake core components of major NLP tasks.


Indicative assessments in this unit are as follows: (1) programming assignment and (2) final examination. Further information is available in the unit outline.

Student may be offered supplementary assessment in this unit if they meet the eligibility criteria.

Unit Coordinator(s)
Dr Siwen Luo
Unit rules
Enrolment in
HON-CMSSE Computer Science and Software Engineering
or 62510 Master of Information Technology
or 62530 Master of Data Science
or 62550 Master of Professional Engineering
or BH008 Bachelor of Advanced Computer Science [Honours]
or ( Bachelor of Engineering (Honours) or an associated Combined Degree
and 96 points
and Successful completion of
CITS1401 Computational Thinking with Python
or CITX1401 Computational Thinking with Python
or CITS2401 Computer Analysis and Visualisation
Contact hours
Lectures: 2-hours per week
Laboratories: 2-hours per week.
From 2025 this unit will be offered in semester 2 only.
  • The availability of units in Semester 1, 2, etc. was correct at the time of publication but may be subject to change.
  • All students are responsible for identifying when they need assistance to improve their academic learning, research, English language and numeracy skills; seeking out the services and resources available to help them; and applying what they learn. Students are encouraged to register for free online support through GETSmart; to help themselves to the extensive range of resources on UWA's STUDYSmarter website; and to participate in WRITESmart and (ma+hs)Smart drop-ins and workshops.
  • Unit readings, including any essential textbooks, are listed in the unit outline for each unit, one week prior the commencement of study. The unit outline will be available via the LMS and the UWA Handbook one week prior the commencement of study. Reading lists and essential textbooks are subject to change each semester. Information on essential textbooks will also be made available on the Essential Textbooks. This website is updated regularly in the lead up to semester so content may change. It is recommended that students purchase essential textbooks for convenience due to the frequency with which they will be required during the unit. A limited number of textbooks will be made available from the Library in print and will also be made available online wherever possible. Essential textbooks can be purchased from the commercial vendors to secure the best deal. The Student Guild can provide assistance on where to purchase books if required. Books can be purchased second hand at the Guild Secondhand bookshop (second floor, Guild Village), which is located on campus.
  • Contact hours provide an indication of the type and extent of in-class activities this unit may contain. The total amount of student work (including contact hours, assessment time, and self-study) will approximate 150 hours per 6 credit points.