Overview
- Describes models and architectures that have been essential in the recent progress of Natural Language Processing
- Focuses on techniques to build feed-forward, recurrent and transformer networks with scikit-learn, Keras, PyTorch
- Includes multiple code examples to solve tasks such as classification, sequence annotation or translation
Part of the book series: Cognitive Technologies (COGTECH)
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Keywords
- Annotation Schemes
- BERT
- Keras
- Information Theory
- Machine Learning
- Machine Translation
- Named Entity Recognition
- Natural Language Processing
- Neural Networks
- NumPy
- Part-of-Speech Tagging
- Python
- PyTorch
- scikit-learn
- Transformer
- Text Segmentation
- Tokenization
- Word Embeddings
- word2vec
About this book
Since the last edition of this book (2014), progress has been astonishing in all areas of Natural Language Processing, with recent achievements in Text Generation that spurred a media interest going beyond the traditional academic circles. Text Processing has meanwhile become a mainstream industrial tool that is used, to various extents, by countless companies. As such, a revision of this book was deemed necessary to catch up with the recent breakthroughs, and the author discusses models and architectures that have been instrumental in the recent progress of Natural Language Processing.
As in the first two editions, the intention is to expose the reader to the theories used in Natural Language Processing, and to programming examples that are essential for a deep understanding of the concepts. Although present in the previous two editions, Machine Learning is now even more pregnant, having replaced many of the earlier techniques to process text. Many new techniques build on the availability of text.Using Python notebooks, the reader will be able to load small corpora, format text, apply the models through executing pieces of code, gradually discover the theoretical parts by possibly modifying the code or the parameters, and traverse theories and concrete problems through a constant interaction between the user and the machine. The data sizes and hardware requirements are kept to a reasonable minimum so that a user can see instantly, or at least quickly, the results of most experiments on most machines.
The book does not assume a deep knowledge of Python, and an introduction to this language aimed at Text Processing is given in Ch. 2, which will enable the reader to touch all the programming concepts, including NumPy arrays and PyTorch tensors as fundamental structures to represent and process numerical data in Python, or Keras for training Neural Networks to classify texts. Covering topics like Word Segmentation and Part-of-Speech and Sequence Annotation, the textbook also gives an in-depth overview of Transformers (for instance, BERT), Self-Attention and Sequence-to-Sequence Architectures.
Authors and Affiliations
About the author
Pierre Nugues is a professor in the Dept. of Computer Science of Lund University. His research is focused on natural language processing for advanced user interfaces and spoken dialogue. This includes the design and the implementation of conversational agents within a multimodal framework and text visualization. He led the team that designed a navigation agent – Ulysse – that enables a user to navigate in a virtual reality environment using language, and the team that designed the CarSim system that generates animated 3D scenes from written texts. He has taught natural-language processing and computational linguistics at the following institutions: ISMRA, Caen, France; University of Nottingham, UK; Staffordshire University, UK; FH Konstanz, Germany; Lund University, Sweden and Ghent University, Belgium.
Bibliographic Information
Book Title: Python for Natural Language Processing
Book Subtitle: Programming with NumPy, scikit-learn, Keras, and PyTorch
Authors: Pierre M. Nugues
Series Title: Cognitive Technologies
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024
Hardcover ISBN: 978-3-031-57548-8Due: 25 June 2024
Softcover ISBN: 978-3-031-57551-8Due: 25 June 2024
eBook ISBN: 978-3-031-57549-5Due: 25 June 2024
Series ISSN: 1611-2482
Series E-ISSN: 2197-6635
Edition Number: 3
Number of Pages: XXV, 518
Number of Illustrations: 36 b/w illustrations, 53 illustrations in colour