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008			230427s2013 sz | s |||| 0|eng d
020			_a9783031021497
024	7		_a10.1007/978-3-031-02149-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.N38 _b2013 EB
100	1		_aSøgaard, Anders, _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687413 _d1981-
245	1	0	_aSemi-Supervised Learning and Domain Adaptation in Natural Language Processing _cby Anders Søgaard
250			_a1st edition 2013
264		1	_aCham _bSpringer International Publishing _c2013
300			_a1 recurso en línea (X, 93 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		_aIntroduction -- Supervised and Unsupervised Prediction -- Semi-Supervised Learning -- Learning under Bias -- Learning under Unknown Bias -- Evaluating under Bias.
520			_aThis 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.
988			_aSynthesis Collection of Technology_2013
650		7	_2embne _9678605 _aLenguajes de programación
650		7	_2embne _9166090 _aAprendizaje automático
776	0	8	_iPrinted edition: _z9783031010217
776	0	8	_iPrinted edition: _z9783031032776
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02149-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b04/2023 _dz _eIG _zSI