你好! Shipping to Taiwan with premium packaging for just NT$300 

Ship to
Taiwan
0

My Account

  • argentina
  • chile
  • colombia
  • españa
  • méxico
  • perú
  • estados unidos
  • internacional

menu

Select your country

Americas

Europe

Rest of the world

portada Semi-Supervised Learning and Domain Adaptation in Natural Language Processing
Type
Physical Book
Author
Publisher
Language
English
Pages
93
Format
Paperback
Dimensions
23.5x19.1x0.6 cm
Weight
0.20 kg.
ISBN13
9783031010217
Edition No.
1
Categories

Semi-Supervised Learning and Domain Adaptation in Natural Language Processing

Anders Søgaard (Author) · Springer · Paperback

Semi-Supervised Learning and Domain Adaptation in Natural Language Processing - Søgaard, Anders

Cheaper New Book Imported to Taiwan
Delivery: 30 Oct - 09 Nov Shipping: 15 to 16 business days.
NT$ 1,081
Faster New Book Imported to Taiwan
Delivery: 16 Oct - 26 Oct Shipping: 5 to 6 business days.
NT$ 1,291
NT$ 1,081

Choose the list to add your product or create one New List

Go to My Wishlists
Calculate shipping cost

Synopsis "Semi-Supervised Learning and Domain Adaptation in Natural Language Processing"

This book introduces basic supervised learning algorithms applicable to natural language processing (NLP) and shows how the performance of these algorithms can often be improved by exploiting the marginal distribution of large amounts of unlabeled data. One reason for that is data sparsity, i.e., the limited amounts of data we have available in NLP. However, in most real-world NLP applications our labeled data is also heavily biased. This book introduces extensions of supervised learning algorithms to cope with data sparsity and different kinds of sampling bias. 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.

Customers reviews

More customer reviews
  • 0% (0)
  • 0% (0)
  • 0% (0)
  • 0% (0)
  • 0% (0)

Frequently Asked Questions about the Book

Is the book original?

All books in our catalog are Original.

In what language is the book written?

The book is written in English.

What is the format of this book?

The binding of this edition is Paperback.

Questions and Answers about the Book

Do you have a question about the book? Login to be able to add your own question.

Opinions about Bookdelivery

More customer reviews