Natural language processing (NLP) is a crucial technology in the era of information age. Exciting advancements in natural language processing (NLP) have recently emerged, enabling systems that can perform tasks such as text translation, question answering, and spoken conversations. This course aims to provide students with a foundational understanding of NLP, including standard frameworks, algorithms, and techniques used to solve various NLP problems. The curriculum will cover topics like language modeling, representation learning, text classification, sequence tagging, syntactic parsing, machine translation, and question answering, with a particular focus on recent deep learning approaches. Through this course, students will receive a comprehensive introduction to NLP concepts, methods, algorithms, applications and state-of-the-art methods research in deep learning for 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 |
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