DAI503 Natural Language Processing (T/651/0607) Assignment Brief 2026
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DAI503 Assignment Brief
| Qualification | Qualifi Level 5 Diploma in Artificial Intelligence (610/3935/4) |
|---|---|
| Unit Code | DAI503 |
| Unit Title | Natural Language Processing |
| Unit Reference | T/651/0607 |
| Credits | 20 |
| TQT | 200 |
| GLH | 120 |
Assignment Aim
In this unit students will develop fundamental knowledge of skills in Natural Language Processing (NLP). Students will cover the fundamental concepts and algorithms commonly used for NLP. They will use Python libraries for NLP to build a search algorithm for extracting information from raw text. Students will have the opportunity to perform Sentiment Analysis to predict stakeholder sentiment.
Learning Outcomes and Assignment Criteria
| Learning Outcomes
When awarded credit for this unit, a learner will: |
Assessment Criteria
Assessment of this learning outcome will require a learner to demonstrate that they can: |
| 1.Understand fundamental concepts in Natural Language Processing (NLP). | 1.1 Define key concepts and the various challenges associated with NLP. |
| 1.2 Explain preprocessing of textual data and the tokenization techniques for NLP analysis. | |
| 1.3 Discuss various key NLP libraries, frameworks and other related tools. | |
| 2. Be able to use NLP libraries, frameworks, and other tools in text representations.
|
2.1 Implement NLP libraries, frameworks, and related tools on a sample text representation scenario |
| 2.2 Review the application of Sentiment Analysis using machine learning and other appropriate NLP methods | |
| 2.3 Use appropriate NLP methods to analyse public sentiment within a given business scenario | |
| 3. Be able to use NLP methods and techniques to carry out sentiment analysis.
|
3.1 Compare the strength and weakness of different NLP methods and applications |
| 3.2 Use appropriate NLP techniques to analyse stakeholder sentiment within a chosen real-life business scenario. | |
| 3.3 Examine output produced by NLP against results from a conducted survey using sentiment analysis. |
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