41043 Natural Language Processing
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Credit points: 6 cp
Subject level:
Undergraduate
Result type: Grade and marksRequisite(s): 41040 Introduction to Artificial Intelligence OR 31250 Introduction to Data Analytics
Description
Natural Language Processing (NLP) develops statistical techniques and algorithms to automatically process natural languages (such as English). It includes a number of AI areas, such as text understanding and summarization, machine translation, and sentiment analysis. This subject introduces the foundations of technologies in NLP and their application to practical problems. 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 projects.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Explain the advantages and disadvantages of different NLP technologies and their applicability in different business situations. |
|---|---|
| 2. | Use NLP technologies to explore and gain a broad understanding of text data. |
| 3. | Use NLP methods to analyse sentiment of a text document. |
| 4. | Use NLP methods to perform topic modelling. |
| 5. | Organise and implement a NLP project in a business environment. |
| 6. | Interpret the results of a NLP project. |
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the development of the following Course Intended Learning Outcomes (CILOs):
- Socially Responsible: FEIT graduates identify, engage, interpret and analyse stakeholder needs and cultural perspectives, establish priorities and goals, and identify constraints, uncertainties and risks (social, ethical, cultural, legislative, environmental, economics etc.) to define the system requirements. (B.1)
- Design Oriented: FEIT graduates apply problem solving, design and decision-making methodologies to develop components, systems and processes to meet specified requirements. (C.1)
- Technically Proficient: FEIT graduates apply abstraction, mathematics and discipline fundamentals, software, tools and techniques to evaluate, implement and operate systems. (D.1)
Teaching and learning strategies
This subject includes combined workshop and laboratory sessions (2 hours) and research and development work for the assignments. The workshop/laboratory sessions will have both individual and group activities, covering NLP theories and algorithms, hands-on exercise on NLP tools, and the understanding and interpretation of NLP project results.
Students are expected to read the program section of the subject outline for instructions regarding required pre-reading and preparation to be undertaken before each class. Pre-reading materials and activities for workshops will be made available on UTSOnline. During the workshop activities, students will receive various types of feedback in a collaborative and discursive learning context, from their fellow peers and lecturers/tutors. In the assessments, students will need to collaborate to propose research ideas and form groups to develop a research project.
Content (topics)
- Text processing and normalization
- Part-of-speech tagging
- Language and topic modelling
- Sentiment Analysis
- Machine Translation
- Deep learning models for NLP
Assessment
Assessment task 1: Career Understanding and Analysis
| Intent: | To identify the skill sets needed in a related industry and produce a report. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1 and 2 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): B.1 and D.1 |
| Type: | Report |
| Groupwork: | Individual |
| Weight: | 20% |
| Length: | A report of 6 pages, in an 11 point font. |
Assessment task 2: Research Report
| Intent: | To identify a research problem and produce a report. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 2, 3 and 6 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): B.1 and D.1 |
| Type: | Report |
| Groupwork: | Group, group assessed |
| Weight: | 30% |
| Length: | A report of 10 pages, in an 11 point font. |
Assessment task 3: Project Development
| Intent: | To develop a project report and a presentation. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 4, 5 and 6 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): B.1, C.1 and D.1 |
| Type: | Report |
| Groupwork: | Group, individually assessed |
| Weight: | 50% |
| Length: | A report of 12 pages, in an 11 point font, plus a 15 minute oral defence. |
Minimum requirements
To pass this subject, students must achieve an overall mark of 50% or greater.
Required texts
Jurafsky, David, and James H. Martin. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition. Upper Saddle River, NJ: Prentice-Hall, 2000. ISBN: 0130950696.
Manning, Christopher D., and Hinrich Schütze. Foundations of Statistical Natural Language Processing. Cambridge, MA: MIT Press, 1999. ISBN: 0262133601.
Recommended texts
NLP tutorial: https://www.upf.edu/web/mtg/nlp-tutorial
Foundations of Statistical Natural Language Processing: https://nlp.stanford.edu/fsnlp
The structure of modern English: https://muse.jhu.edu/article/19425