一部值得精读的著作,将神经网络方法和自然语言处理的相关课题紧密的联合了起来.介绍了神经网络的构建细节和机器学习的一些基本内容,并且包含了RNN,CNN等主流神经网络在NLP中的应用实例,另外2017年新书,有最新的学术信息.
Neural Network Methods for Natural Language Processing Synthesis Lectures on Human Language Technologies Editor Graeme Hirst, University of Toronto Synthesis Lectures on Human Language Technologies is edited by Graeme Hirst of the University of Toronto. The series consists of 50-to 150-page monographs on topics relating to natural language processing, computational linguistics, information retrieval, and spoken language understanding Emphasis is on important new techniques, on new applications, and on topics that combine two or more hlt subfields Neural Network Methods for Natural Language processing Yoav goldberg 2017 Syntax-based Statistical Machine Translation Philip williams, Rico Sennrich, Matt post, and Philipp koehn 2016 Domain-Sensitive Temporal Tagging Jannik Strotgen and Michael gertz 2016 Linked Lexical Knowledge Bases: Foundations and Applications ryna Gurevych, Judith Eckle-Kohler, and Michael Matuschek 016 Bayesian Analysis in Natural Language Processing Shay cohen 2016 Metaphor: A Computational Perspective Tony Veale, Ekaterina Shutova, and Beata beigman Klebanov 2016 Grammatical Inference for Computational Linguistics Jeffrey Heinz, Colin de la Higuera, and Menno van Zaanen 2015 Automatic Dctection of Verbal Deception Eileen Fitzpatrick, Joan Bachenko, and Tommaso Fornaciari 2015 Natural language Proccssing for Social Media Atefeh Farzindar and Diana Inkpen 2015 Semantic Similarity from Natural Language and Ontology analysis Sebastien Harispe, Sylvie Ranwez, Stefan Janaqi, and Jacky Montmain 2015 Learning to Rank for Information Retrieval and Natural Language Processing, Second editio Hang 2014 Ontology-Based Interpretation of Natural Language Philipp cimiano, Christina Unger, and john mccrae 2014 Automated Grammatical Error Detection for Language Learners, Second Edition Claudia leacock, Martin Chodorow, Michael Gamon, and Joel Tetreault Web Corpus Construction Roland Schafer and felix bildhauer 2013 Recognizing Textual Entailment: Models and Applications Ido dagan. Dan roth Mark sammons and fabio massimo zanzotto 2013 Linguistic Fundamentals for Natural Language Processing: 100 Essentials from Morphology and Syntax Emily m. bender Serni-Supervised Learning and Dornain Adaptation in Natural Language Processing Anders s 2013 Semantic relations between nominals Vivi Nastase, Preslav Nakov, Diarmuid O Seaghdha, and Stan Szpakowicz 2013 Computational modeling of narrative Inderjeet mani Natural Language Processing for Historical Texts Michael p ki 2012 Sentiment Analysis and Opinion Mining Bing liu 2012 Discourse Processing Manfred s 2011 Bitext Alignment Jorg tiedemann 2011 Linguistic Structure Prediction Noah A. Smith Learning to Rank for Information Retrieval and Natural Language Processing Hang li 2011 Computational Modeling of I luman Language Acquisition Afra Alishahi 2010 Introduction to Arabic Natural Language Processing Nizar y habash 2010 Cross-Language Information Retrieval 1an-run Nie 2010 Automated Grammatical Error Detection for Language Learners Claudia Leacock, Martin Chodorow, Michael Gamon, and Joel Tetreault 2010 Data-Intensive Text Processing with Mapreduce Jimmy Lin and Chris Dyer 2010 Scmantic role labeling Martha palmer. Daniel gildea, and nianwen Xue 2010 Spoken Dialogue Systcms Kristiina Jokinen and Michael McTear 2009 Introduction to Chinese Natural Language processing Kam-Fai Wong, Wenjie Li, Ruifeng Xu, and Zheng-sheng /hang 2009 Introduction to Linguistic Annotation and Text analytics Graham Wilcock 2009 Dependency Parsing Sandra Kiibler, Ryan McDonald, and Joakim Nivre 200 Statistical Language Models for Information Retrieval Cheng xiang zh 2008 Copyright(c 2017 by morgan claypool All rights reserved. No part of this publication may be reproduced, stored in a retrieval sy stem, or transmitted any form or by any means--electronic, mechanical, photocopy, recording, or any other except for brief quotations in printed reviews, without the prior permission of the publisher Neural Network Methods for Natural Language Processing rg www.morga.nclaypool.com ISBN:9781627052986 paperback ISBN:9781627052955 ebook DOI10.2200/S00762EDIV01Y201703HLT037 A Publication in the morgan Claypool Publ SYNTTIESIS LECTURES ONUMAN LANGUAGE TECMNOLOGIES Series Editor: Graeme Hirst, Universily o'Toronlo Series Issn Print 1947-4040 El Neural Network Methods for Natural Language Processing Yoav goldberg Bar Ilan University SYNTHESIS LECTURES ON HUMAN LANGUAGE TECHNOLOGIES #37 MORGAN &CLAYPOOL PUBLISherS ABSTRACT Neural networks are a fanily 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 The second part of the book(Parts III and Iv) introduces more specialized neural net work architectures, including 1D convolutional neural networks, recurrent neural networks ditioned-generation models, and based models. These archit 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 d the prospects of multi-task le KEYWORDS natural language processing, machine learning, supervised learning, deep learning, neural networks, word embeddings, recurrent neural networks, sequence to sequence models