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020			_a9789811300622
024	7		_a10.1007/978-981-13-0062-2 _2doi
040			_bspa _dES-MaUEC _cES-MaUEC
050		4	_aQA76.9 .N38 _b2019 EB
100	1		_aWhite, Lyndon _eautor _9671003
245	1	0	_aNeural representations of natural language _cby Lyndon White, Roberto Togneri, Wei Liu, Mohammed Bennamoun
264		1	_aSingapore _bSpringer Singapore : _bImprint: Springer _c2019
300			_a1 recurso en línea (XIV, 122 páginas) _b36 ilustraciones, 31 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2rda
490	0		_aStudies in Computational Intelligence _x1860-949X _v783
490	0		_aIntelligent Technologies and Robotics (Springer-42732)
505	0		_aIntroduction -- Machine Learning for Representations -- Current Challenges in Natural Language Processing -- Word Representations -- Word Sense Representations -- Phrase Representations -- Sentence representations and beyond -- Character-Based Representations -- Conclusion.
520	3		_aThis book offers an introduction to modern natural language processing using machine learning, focusing on how neural networks create a machine interpretable representation of the meaning of natural language. Language is crucially linked to ideas - as Webster's 1923 "English Composition and Literature" puts it: "A sentence is a group of words expressing a complete thought". Thus the representation of sentences and the words that make them up is vital in advancing artificial intelligence and other "smart" systems currently being developed. Providing an overview of the research in the area, from Bengio et al.'s seminal work on a "Neural Probabilistic Language Model" in 2003, to the latest techniques, this book enables readers to gain an understanding of how the techniques are related and what is best for their purposes. As well as a introduction to neural networks in general and recurrent neural networks in particular, this book details the methods used for representing words, senses of words, and larger structures such as sentences or documents. The book highlights practical implementations and discusses many aspects that are often overlooked or misunderstood. The book includes thorough instruction on challenging areas such as hierarchical softmax and negative sampling, to ensure the reader fully and easily understands the details of how the algorithms function. Combining practical aspects with a more traditional review of the literature, it is directly applicable to a broad readership. It is an invaluable introduction for early graduate students working in natural language processing; a trustworthy guide for industry developers wishing to make use of recent innovations; and a sturdy bridge for researchers already familiar with linguistics or machine learning wishing to understand the other.
988			_aPrimersemestre_2019_Robotics
650		7	_2embne _aProceso en lenguaje natural (Informática) _9158738
650		7	_2embne _aAprendizaje automático _9166090
650		7	_2embne _aRedes neuronales artificiales _9678664
700	1		_aTogneri, Roberto _eautor _9671004
700			_aLiu, Wei _eautor _9671005
700	1		_aBennamoun, M. _eautor _9671006 _q(Mohammed)
776	0	8	_iPrinted edition: _z9789811300615
776	0	8	_iPrinted edition: _z9789811300639
776	0	8	_iPrinted edition: _z9789811343209
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-13-0062-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_aSI _cm _dz _feng _ggw _h0 _b10/2019 _eel _zSI