Administrative Information
| Title | Natural Language Processing |
| Duration | 60-70 mins |
| Module | A |
| Lesson Type | Practical |
| Focus | Practical - AI Modelling |
| Topic | Text Classification, Sentiment Classification |
Keywords
Natural Language Processing,Naive Bayes Classifier,
Learning Goals
- Student will understand the basics of the core NLP techniques
- Student gets familiar with the use of a Naive Bayes Classifier
Expected Preparation
Learning Events to be Completed Before
None.
Obligatory for Students
- Basic Python Programming
- Basic Statistics
Optional for Students
References and background for students
- Natural Language Toolkit
- Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press. 2008
- Jurafskly D., Martin J. H. - An Introduction to NLP, Computational Linguistics, and Speech Recognition
Recommended for Teachers
- Natural Language Toolkit
- Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press. 2008
- Jurafskly D., Martin J. H. - An Introduction to NLP, Computational Linguistics, and Speech Recognition
Lesson materials
Instructions for Teachers
This learning event consist of laboratory tasks that shall be solved by the students with the help of the leading instructor.
Outline
| Duration (Min) | Description | Concepts | Activity | Material |
|---|---|---|---|---|
| 5 | Word Tokenisation | |||
| 5-10 | Pandas DataFrames | |||
| 10 | Bag of Words | |||
| 10 | Tokenisation with a Regular Expression | |||
| 10 | N-gram Models | |||
| 5 | Stopwords | |||
| 10-15 | Normalisation, Stemming and Lemmatisation | |||
| 5-10 | Sentiment Analysis |
Acknowledgements
The Human-Centered AI Masters programme was Co-Financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068.