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999			_c387377 _d387377
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003			ES-MaUEC
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007			cr nn 008mamaa
008			220601s2017 sz | s |||| 0|eng d
020			_a9783031021657
024	7		_a10.1007/978-3-031-02165-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.N38 _b2017 EB
100	1		_aGoldberg, Yoav, _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687407 _d1980-
245	1	0	_aNeural Network Methods for Natural Language Processing _cby Yoav Goldberg
250			_a1st edition 2017
264		1	_aCham _bSpringer International Publishing _c2017
300			_a1 recurso en línea (CCXCII, 20 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Human Language Technologies _x1947-4059
505	0		_aPreface -- Acknowledgments -- Introduction -- Learning Basics and Linear Models -- 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 -- Pre-trained Word Representations -- Using Word Embeddings -- Case Study: A Feed-forward Architecture for Sentence -- 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 -- Modeling with Recurrent Networks -- Conditioned Generation -- Modeling Trees with Recursive Neural Networks -- Modeling Trees with Recursive Neural Networks -- Structured Output Prediction -- Cascaded, Multi-task and Semi-supervised Learning -- Conclusion -- Bibliography -- Author's Biography.
520			_aNeural 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. 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.
988			_aSynthesis Collection of Technology_2017
650		7	_2embne _9158738 _aProceso en lenguaje natural (Informática)
650		7	_2embne _9678664 _aRedes neuronales artificiales
776	0	8	_iPrinted edition: _z9783031001765
776	0	8	_iPrinted edition: _z9783031010378
776	0	8	_iPrinted edition: _z9783031032936
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02165-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _esc _zSI