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The Social Origins of Language

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Presents a new theoretical framework for the origins of human language and sets key issues in language evolution in their wider context within biological and cultural evolution


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Preposition Placement in English: A Usage-Based Approach

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This is the first study that empirically investigates preposition placement across all clause types. The study compares first-language (British English) and second-language (Kenyan English) data and will therefore appeal to readers interested in world Englishes. Over 100 authentic corpus examples are discussed in the text, which will appeal to those who want to see 'real data'


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Book Information

   

Title: Semi-Supervised Learning and Domain Adaptation in Natural Language Processing
Written By: Anders Søgaard
URL: http://www.morganclaypool.com/doi/abs/10.2200/S00497ED1V01Y201304HLT021
Series Title: Synthesis Lectures on Human Language Technologies
Description:

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.

Publication Year: 2013
Publisher: Morgan & Claypool Publishers
Review: Not available for review. If you would like to review a book on The LINGUIST List, please login to view the AFR list.
BibTex: View BibTex record
Linguistic Field(s): Computational Linguistics
Issue: All announcements sent out by The LINGUIST List are emailed to our subscribers and archived with the Library of Congress.
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Versions:
Format: Paperback
ISBN-13: 9781608459858
Pages: 103
Prices: U.S. $ 40.00

 
 
Format: Electronic
ISBN-13: 9781608459865
Pages: 103
Prices: U.S. $ 30.00