Text Mining 2023-2024
Course schedule
The course weeks consist of: a lecture, literature to read, and either a practical exercise (tutorial style) or a hand-in assignment.
The lectures are on Wednesday, 9.00-10.45.
Location: GORL / 01 (Gorlaeus building)
The literature will be distributed on Brightspace. The majority of the chapters comes from this book, abbreviated as J&M in the course schedule below.
- J&M: Dan Jurafsky and James H. Martin, Speech and Language Processing (3rd ed), 2023
| Week | Lecture | Literature | Exercise / assignment |
|---|---|---|---|
| 1 (6 Sept) | Introduction | ||
| 2 (13 Sept) | Text processing | J&M chapter 2. Regular Expressions, Text Normalization, Edit Distance | Exercise: Chapter 1 of "Advanced NLP with Spacy" |
| 3 (20 Sept) | Vector Semantics | (optional) J&M sections 7.1, 7.2, 7.3. Neural Networks J&M chapter 6. Vector Semantics | Exercise: Word Embedding Tutorial: Word2vec with Gensim |
| 4 (27 Sept) | Text categorization | J&M chapter 4. Naive Bayes Classification | Exercise: Text classification tutorial (sklearn) |
| 5 (4 Oct) | Data collection and annotation | Finin (2010). Annotating Named Entities in Twitter Data with Crowdsourcing McHugh (2012). Interrater reliability: the kappa statistic | Assignment 1. Text classification (deadline 10 October) |
| 6 (11 Oct) | Neural NLP and transfer learning | J&M chapter 10. Transformers and Pretrained Language Models | Exercise: Chapters 2 and 3 of the Huggingface NLP course |
| (18 Oct) | No lecture | ||
| 7 (25 Oct) | Information Extraction | J&M chapter 8. Sequence Labeling for Parts of Speech and Named Entities | Exercise: Token classification tutorial in the Huggingface NLP course |
| 8 (1 Nov) | Sentiment analysis & Stance detection | J&M chapter 11. Fine-Tuning and Masked Language Models | Assignment 2. Information Extraction (deadline 7 November) |
| 9 (8 Nov) | Topic Modelling & Text summarization | Zhang et al. (2020). PEGASUS (Abstractive Summarization) | Exercise: Summarization tutorial in the Huggingface NLP course |
| 10 (15 Nov) | Generative large language models | Brown et al (2020). Language Models are Few-Shot Learners | |
| 11 (22 Nov) | Industrial Text Mining | Guest lecture by Marzieh Fadaee (Cohere): "How multilingualism shapes LLMs and where to go next" | Paper reading for the final assignment |
| 12 (29 Nov) | Exam preparation session | ||
| 13 (6 Dec) | Online lab session | Final assignment (deadline 5 January) | |
| (22 Dec) | Exam | ||
| (2 Feb) | Re-sit |
The assessment of the course consists of a written exam (50% of course grade) and practical assignments (50% of course grade). The practical assignments comprise two smaller assignments (10% each) and one more substantial, final assignment (30%). The grade for the written exam should be 5.5 or higher in order to complete the course. The weighted average grade for the practical assignments should be 5.5 or higher in order to complete the course. If one of the tasks is not submitted the grade for that task is 0. Each assignment has a re-sit opportunity (a later submission). The maximum grade for a re-sit assignment is 6.
Earlier editions of this course
Link to the course page for this course in 2022-2023
Link to the course page for this course in 2021-2022
Link to the course page for this course in 2020-2021
Link to the course page for this course in 2019-2020
Link to the course page for this course in 2018-2019