42850 Natural Language Processing Algorithms
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Credit points: 6 cp
Subject level:
Postgraduate
Result type: Grade and marksRequisite(s): 32130 Fundamentals of Data Analytics
Anti-requisite(s): 41043 Natural Language Processing
Description
Natural Language Processing (NLP) develops statistical techniques and algorithms to automatically process natural languages (such as English), which rely on a number of AI areas, such as, text understanding and summarisation, machine translation, and sentiment analysis. This subject introduces the foundations of technologies in NLP, the current state-of-the-art NLP algorithms, and their application to practical problems. It brings together cutting-edge research with 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. | Identify NLP applications in business and social contexts |
|---|---|
| 2. | Reflect on their own NLP skill development. |
| 3. | Critique NLP algorithms from academic research papers. |
| 4. | Design and implement solutions for real-world NLP problems. |
Teaching and learning strategies
This subject includes combined workshop and laboratory sessions (2 hours) with 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 encouraged to engage in pre-reading materials, which will be made available on UTS Online, and come with prepared questions for the workshops. During the workshop activities, students will receive feedback from the teaching staff and their student peers in a collaborative and discursive learning context.
Content (topics)
- Text processing and word embedding vectors
- Part-of-speech tagging
- Language and topic modelling
- Sentiment Analysis
- Text Summarisation
- Machine Translation
Assessment
Assessment task 1: Career Planning and Analysis
| Intent: | To make sense of current industry needs with respect to NLP-related jobs and skills |
|---|---|
| Type: | Report |
| Groupwork: | Individual |
| Weight: | 20% |
| Length: | A report with 1000 words. |
Assessment task 2: Literature Review and Research Report
| Intent: | To compare, contrast and critique existing NLP algorithms. |
|---|---|
| Type: | Literature review |
| Groupwork: | Group, group assessed |
| Weight: | 30% |
| Length: | A report with 3000 words. |
Assessment task 3: Research Project Development
| Intent: | To design and implement solutions for real-world NLP problems in a research project and interpret the results of their projects. |
|---|---|
| Type: | Project |
| Groupwork: | Group, individually assessed |
| Weight: | 50% |
| Length: | A report of 3500 words including references, plus an oral defence. |
Minimum requirements
In order to pass the subject, a student must achieve an overall mark of 50% or more.
Required texts
Eisenstein J. Introduction to Natural Language Processing. MIT Press; October 2019. ISBN: 9780262042840.
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