[Corpora-List] New book: S=?iso-8859-1?Q?=F8gaard=2C_?=Semi-Supervised Learning and Domain Adaptation in NLP

[Graeme Hirst]

BOOK ANNOUNCEMENT

Semi-Supervised Learning and Domain Adaptation in Natural Language Processing

by Anders Søgaard, University of Copenhagen

Synthesis Lectures on Human Language Technologies #21 (Morgan & Claypool Publishers), 2013, x+93 pages

Abstract

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.

Table of Contents: Introduction / Supervised and Unsupervised Prediction / Semi-Supervised Learning / Learning under Bias / Learning under Unknown Bias / Evaluating under Bias

http://www.morganclaypool.com/doi/abs/10.2200/S00497ED1V01Y201304HLT021


This title is available online without charge to members of institutions that have licensed the Synthesis Digital Library of Engineering and Computer Science.  Members of licensing institutions have unlimited access to download, save, and print the PDF without restriction; use of the book as a course text is encouraged.  To find out whether your institution is a subscriber, visit http://www.morganclaypool.com/page/licensed, or just click on the book's URL above from an institutional IP address and attempt to download the PDF.  Others may purchase the book from this URL as a PDF download for US$30 or in print for US$40.  Printed copies are also available from Amazon and from booksellers worldwide at approximately US$45 or local currency equivalent. 


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::::  Graeme Hirst * Series editor, Synthesis Lectures in Human Language Technologies
::::  University of Toronto * Department of Computer Science




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