The objective of this course is to introduce and teach the fundementals of problems, theories, algorithms and applications of Natural Language Processing (NLP).
Data Structures
Speech and Language Processing (3rd ed. draft) Dan Jurafsky and James H. Martin (html). |
- Jacob Eisenstein, Natural Language
Processing (2018)
- Yoav
Goldberg, Neural Network Methods for Natural Language Processing
(2017)
- James Pustejovsky and Amber Stubbs, Natural Language Annotation
for Machine Learning (2012)
- Hands-On Machine Learning with Scikit-Learn and TensorFlow (https://github.com/ageron/handson-ml3)
- Deep Learning for Coders with fastai and PyTorch (https://github.com/fastai/fastbook)
- HuggingFace NLP Course (html)
- Practical Natural Language
Processing (2020) (https://github.com/practical-nlp)
-
Natural Language Processing with Transformers, Revised Edition
(2022) (https://github.com/nlp-with-transformers )
- Natural Language Processing with PyTorch (2019) (https://github.com/delip/PyTorchNLPBook)
Evaluation Tool (*) | Weight in % |
---|---|
Assignments, Presentations and Projects |
30 |
In-term Exams - 1 Midterm |
30 |
Final | 40 |
WEEK | TOPIC(S) |
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1 | Introduction |
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15 |