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</o:shapelayout></xml><![endif]--></head><body lang=EN-US link="#0563C1" vlink="#954F72"><div class=WordSection1><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif'>Dear Connectionists Listserv Member,<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif'>I am pleased to announce a new title in Morgan & Claypool’s series on Human Language Technologies:<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif'>Neural Network Methods for Natural Language Processing<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif'>Yoav Goldberg, Bar Ilan University<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif'>Paperback ISBN: 9781627052986, $74.95<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif'>eBook ISBN: 9781627052955<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif'>April 2017, 309 pages<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif'>http://dx.doi.org/10.2200/S00762ED1V01Y201703HLT037 <o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif'>Abstract:<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif'>Neural networks are a family of powerful machine learning models. This book focuses on the application of neural network models to natural language data. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries.<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif'>The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree shaped networks, structured prediction, and the prospects of multi-task learning.<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif'>Table of Contents: Preface / Acknowledgments / Introduction / Learning Basics and Linear Models / From Linear Models to Multi-layer Perceptrons / Feed-forward Neural Networks / Neural Network Training / Features for Textual Data / Case Studies of NLP Features / From Textual Features to Inputs / Language Modeling / Pre-trained Word Representations / Using Word Embeddings / Case Study: A Feed-forward Architecture for Sentence Meaning Inference / Ngram Detectors: Convolutional Neural Networks / Recurrent Neural Networks: Modeling Sequences and Stacks / Concrete Recurrent Neural Network Architectures / Modeling with Recurrent Networks / Conditioned Generation / Modeling Trees with Recursive Neural Networks / Structured Output Prediction / Cascaded, Multi-task and Semi-supervised Learning / Conclusion / Bibliography / Author's Biography<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif'>Visit this title’s abstract page on our website:<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif'>http://www.morganclaypool.com/doi/abs/10.2200/S00762ED1V01Y201703HLT037 <o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif'>Morgan & Claypool Bookstore Link:<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif'>http://www.morganclaypoolpublishers.com/catalog_Orig/product_info.php?products_id=1056<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif'>Series: Synthesis Lectures on Human Language Technologies<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif'>Editor: Graeme Hirst, University of Toronto<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif'>http://www.morganclaypool.com/toc/hlt/10/1<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif'>Please e-mail me if you have any questions.<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif'>Thank you,<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif'>Bebe Barrow<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif;color:black'>--<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif;color:black'>Bebe Barrow<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif;color:black'>Sales & Marketing Assistant<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif;color:black'>Morgan & Claypool Publishers<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Times New Roman",serif'><a href="mailto:carreon@morganclaypool.com">barrow@morganclaypool.com</a><o:p></o:p></span></p><p class=MsoNormal><o:p> </o:p></p></div></body></html>