42173 Advanced Natural Language Processing
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Credit points: 6 cp
Subject level:
Postgraduate
Result type: Grade and marksRequisite(s): 42850 Natural Language Processing Algorithms
Description
Natural language processing is at the heart of many computing applications, such as voice-command systems, from major IT companies. This subject introduces advanced topics and technologies in natural language processing, and their applications to real-world problems. The subject builds on the previous NLP subject to give a deep understanding on how more powerful NLP algorithms work. It also introduces deep learning-based approaches that underpin the most currently advanced NLP technologies.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Demonstrate an understanding of the advantages and limitations of several advanced natural language processing methods. (D.1) |
|---|---|
| 2. | Review technologies used in advanced natural language processing literature, and perform self-reflection. (F.1) |
| 3. | Develop an advanced natural language processing project for a real-world problem. (C.1) |
| 4. | Communicate project results in both oral and written forms. (E.1) |
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the development of the following Course Intended Learning Outcomes (CILOs):
- Design Oriented: FEIT graduates apply problem solving, design thinking and decision-making methodologies in new contexts or to novel problems, to explore, test, analyse and synthesise complex ideas, theories or concepts. (C.1)
- Technically Proficient: FEIT graduates apply theoretical, conceptual, software and physical tools and advanced discipline knowledge to research, evaluate and predict future performance of systems characterised by complexity. (D.1)
- Collaborative and Communicative: FEIT graduates work as an effective member or leader of diverse teams, communicating effectively and operating autonomously within cross-disciplinary and cross-cultural contexts in the workplace. (E.1)
- Reflective: FEIT graduates critically self-review their own and others' performance with a high level of responsibility to improve and practice competently for the benefit of professional practice and society. (F.1)
Teaching and learning strategies
The subject is delivered by online learning material, lectures, and guest lectures. The lectures will provide students with advanced theories and practises in Advanced NLP. The guest lectures will feature current research frontline outcomes, as well as industry and business practises.
Content (topics)
- Overview of natural language processing (NL)
- Deep Neural Networks for NLP
- Convolutional Neural Networks for NLP
- Recurrent Neural Networks for NLP
- Advanced Text Summarisation
- Advanced Neural Translation
- Recommender Systems
- Web Analytics
Assessment
Assessment task 1: Quiz
| Intent: | To demonstrate a clear understanding of the basic theories and algorithms underlying advanced NLP methods |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): D.1 |
| Type: | Quiz/test |
| Groupwork: | Individual |
| Weight: | 30% |
| Length: | 2 hours |
Assessment task 2: Literature review and reflection
| Intent: | To ensure students can think critically about how advanced NLP technologies are developed in the current state-of-the-art literature. |
|---|---|
| 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): D.1 and F.1 |
| Type: | Report |
| Groupwork: | Individual |
| Weight: | 20% |
| Length: | The report will be 1,500 – 2,500 words in an 11-point font. |
Assessment task 3: Research project development
| Intent: | To apply advanced NLP methods to solve a practical real-world problem. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 3 and 4 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): C.1, D.1 and E.1 |
| Type: | Project |
| Groupwork: | Group, individually assessed |
| Weight: | 50% |
| Length: | The report will be 2,500 – 3,500 words in an 11-point font, plus an oral presentation. |
Minimum requirements
In order to pass the subject, a student must achieve an overall mark of 50% or more.
Recommended texts
Eisenstein J. Introduction to Natural Language Processing. MIT Press; October 2019. ISBN: 9780262042840.
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