Buch, Englisch, 103 Seiten, Paperback, Format (B × H): 187 mm x 235 mm
Buch, Englisch, 103 Seiten, Paperback, Format (B × H): 187 mm x 235 mm
Reihe: Synthesis Lectures on Human Language Technologies
ISBN: 978-1-60845-985-8
Verlag: MORGAN & CLAYPOOL
This book is intended to be both readable by first-year students and interesting to the expert audience. My intention was to introduce what is necessary to appreciate the major challenges we face in contemporary NLP related to data sparsity and sampling bias, without wasting too much time on details about supervised learning algorithms or particular NLP applications. I use text classification, part-of-speech tagging, and dependency parsing as running examples, and limit myself to a small set of cardinal learning algorithms. I have worried less about theoretical guarantees (""this algorithm never does too badly"") than about useful rules of thumb (""in this case this algorithm may perform really well""). In NLP, data is so noisy, biased, and non-stationary that few theoretical guarantees can be established and we are typically left with our gut feelings and a catalogue of crazy ideas. I hope this book will provide its readers with both. Throughout the book we include snippets of Python code and empirical evaluations, when relevant.
Autoren/Hrsg.
Weitere Infos & Material
- Introduction
- Supervised and Unsupervised Prediction
- Semi-Supervised Learning
- Learning under Bias
- Learning under Unknown Bias
- Evaluating under Bias