Deep Learning for Natural Language Processing

Vorwort
Provides a clear, accessible introduction to deep learning for natural language processing, covering both practical and theoretical aspects.

Autorentext

Mihai Surdeanu is Associate Professor in the Computer Science Department at the University of Arizona. He works in both academia and industry on NLP systems that process and extract meaning from natural language.

Inhalt

Preface; 1. Introduction; 2. The perception; 3. Logistic regression; 4. Implementing text classfication using perceptron and LR; 5. Feed forward neural networks; 6. Best practices in deep learning; 7. Implementing text classification with feed forward networks; 8. Distributional hypothesis and representation learning; 9. Implementing text classification using word embedding; 10. Recurrent neural networks; 11. Implementing POS tagging using RNNs; 12. Contexualized embeddings and transformer networks; 13. Using transformers with the hugging face library; 14. Encoder-decoder methods; 15. Implementing encoder-decoder methods; 16. Neural architecture for NLP applications; Appendix A: Overview of the python language and the key libraries; Appendix B: Character endcodings: ASCII and unicode.