SIT770 - Natural Language Processing
Unit details
| Year: | 2024 unit information |
|---|---|
| Enrolment modes: | Trimester 1: Burwood (Melbourne), Waurn Ponds (Geelong), Online |
| Credit point(s): | 1 |
| EFTSL value: | 0.125 |
| Unit Chair: | Trimester 1: Mohamed Reda Bouadjenek |
| Prerequisite: | For students enrolled in students in S737 and S770: SIT787 and one of SIT720 or SIT731 For all other students: SIT787 and SIT771 and one of SIT731 or SIT720 |
| Corequisite: | Nil |
| Incompatible with: | SIT330 |
| Typical study commitment: | Students will on average spend 150 hours over the teaching period undertaking the teaching, learning and assessment activities for this unit. This will include educator guided online learning activities within the unit site. |
| Educator-facilitated (scheduled) learning activities - on-campus unit enrolment: | 1 x 3 hour seminar per week |
| Educator-facilitated (scheduled) learning activities - online unit enrolment: | Online independent and collaborative learning including 1 x 2 hour online seminar per week. |
Content
The widespread use of technology has allowed the creation of large quantities of text data because people communicate almost everything in text language: webpages, social media posts, ads, emails, customer service, language translation, virtual agents, medical reports, etc. Thus, the need to exploit this unprecedented quantity of text data is critical for many companies and their business. This has led to the development of Document Processing techniques to extract valuable knowledge from this data, based on fast, effective and scalable information retrieval, text mining, and machine learning algorithms, which are at the heart of this unit focusing on descriptive, predictive, and prescriptive analytics of large-scale text data. In particular, this unit provides students with a deep understanding of the basic theories and techniques used to process large-scale of text document data to understand, search, identify, and extract information from semi-structured text documents. The unit will cover basic technologies for text document analysis, including information retrieval, search and indexing, information extraction, document classification, and natural language understanding.
| ULO | These are the Learning Outcomes (ULO) for this unit. At the completion of this unit, successful students can: | Deakin Graduate Learning Outcomes |
|---|---|---|
| ULO1 | Explain natural language processing concepts and algorithms to a range of technical and non-technical audiences. | GLO1: Discipline-specific knowledge and capabilities |
| ULO2 | Design and develop software solutions to that make effective and informed use of natural language processing tools and frameworks to solve real world problems, and justify the approach taken to a range of technical and non-technical audiences. | GLO1: Discipline-specific knowledge and capabilities |
| ULO3 | Research, apply, and articulate a range of natural language processing approaches for proposed software solutions to a range of technical and non-technical audiences. | GLO2: Communication |
| ULO4 | Effectively regulate and reflect upon learning and present compelling evidence of learning achieved. | GLO6: Self-management |
Assessment
| Assessment Description | Student output | Grading and weighting (% total mark for unit) | Indicative due week |
|---|---|---|---|
| Learning portfolio | Portfolio consisting of programming code, presentations, and reports. | 100% | Week 12 |
The assessment due weeks provided may change. The Unit Chair will clarify the exact assessment requirements, including the due date, at the start of the teaching period.
Hurdle requirement
To be eligible to obtain a pass in this unit, students must meet certain milestones as part of the portfolio.
Learning Resource
The texts and reading list for the unit can be found on the University Library via the link below: SIT770 Note: Select the relevant trimester reading list. Please note that a future teaching period's reading list may not be available until a month prior to the start of that teaching period so you may wish to use the relevant trimester's prior year reading list as a guide only.
Unit Fee Information
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