E-Book Overview
We're finally getting to the point where Computational Linguistics will start to see their titles in the titles. In the past one would have to piggyback off of another discipline to get the information they needed. This book is a must for anyone learning anything statistical in the NLP field. I took a class which covered nearly all of the topics in this book just months before the book came out. I struggled through some of the concepts and spent many a sleepless night going over an academic paper at least one more time getting those concepts down. On the last day of class the professor suggested this new title. I went and bought and most of the hard stuff I had struggled with solidified in my mind. A great feeling! I wish it was the textbook. About the book itself; it does assume the reader is pretty math savvy. Some sections claim they are not breaking down a proof even though the only thing on the page are equations. But on the flip side, Abney does a fantastic job of grounding the terminology before launching into that. The first few chapters are very informative and patient with the reader. It is also excellent if you just need a refresher on any of these topics.
E-Book Content
Computer Science and Data Analysis Series
Semisupervised Learning for Computational Linguistics
Chapman & Hall/CRC
Computer Science and Data Analysis Series The interface between the computer and statistical sciences is increasing, as each discipline seeks to harness the power and resources of the other. This series aims to foster the integration between the computer sciences and statistical, numerical, and probabilistic methods by publishing a broad range of reference works, textbooks, and handbooks. SERIES EDITORS David Madigan, Rutgers University Fionn Murtagh, Royal Holloway, University of London Padhraic Smyth, University of California, Irvine Proposals for the series should be sent directly to one of the series editors above, or submitted to: Chapman & Hall/CRC 23-25 Blades Court London SW15 2NU UK
Published Titles Bayesian Artificial Intelligence Kevin B. Korb and Ann E. Nicholson Pattern Recognition Algorithms for Data Mining Sankar K. Pal and Pabitra Mitra Exploratory Data Analysis with MATLAB® Wendy L. Martinez and Angel R. Martinez Clustering for Data Mining: A Data Recovery Approach Boris Mirkin Correspondence Analysis and Data Coding with Java and R Fionn Murtagh R Graphics Paul Murrell Design and Modeling for Computer Experiments Kai-Tai Fang, Runze Li, and Agus Sudjianto Semisupervised Learning for Computational Linguistics Steven Abney
Computer Science and Data Analysis Series
Semisupervised Learning for Computational Linguistics
Steven Abney University of Michigan Ann Arbor, U.S.A.
Boca Raton London New York
Chapman & Hall/CRC is an imprint of the Taylor & Francis Group, an informa business
Chapman & Hall/CRC Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487‑2742 © 2008 by Taylor & Francis Group, LLC Chapman & Hall/CRC is an imprint of Taylor & Francis Group, an Informa business No claim to original U.S. Government works Printed in the United States of America on acid‑free paper 10 9 8 7 6 5 4 3 2 1 International Standard Book Number‑13: 978‑1‑58488‑559‑7 (Hardcover) This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission, and sources are indicated. A wide variety of references are listed. Reasonable efforts have been made to publish reliable data and information, but the author and the publisher cannot assume responsibility for the validity of all materials or for the conse‑ quences of their use. No part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now know