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008			220601s2022 sz | s |||| 0|eng d
020			_a9783031021817
024	7		_a10.1007/978-3-031-02181-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.T48 _b2022 EB
100	1		_aLin, Jimmy, _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687457 _d1979-
245	1	0	_aPretrained Transformers for Text Ranking : _bBERT and Beyond _cby Jimmy Lin, Rodrigo Nogueira, Andrew Yates
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XVII, 307 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 -- Setting the Stage -- Multi-Stage Architectures for Reranking -- Refining Query and Document Representations -- Learned Dense Representations for Ranking -- Future Directions and Conclusions -- Bibliography -- Authors' Biographies.
520			_aThe goal of text ranking is to generate an ordered list of texts retrieved from a corpus in response to a query. Although the most common formulation of text ranking is search, instances of the task can also be found in many natural language processing (NLP) applications.This book provides an overview of text ranking with neural network architectures known as transformers, of which BERT (Bidirectional Encoder Representations from Transformers) is the best-known example. The combination of transformers and self-supervised pretraining has been responsible for a paradigm shift in NLP, information retrieval (IR), and beyond. This book provides a synthesis of existing work as a single point of entry for practitioners who wish to gain a better understanding of how to apply transformers to text ranking problems and researchers who wish to pursue work in this area. It covers a wide range of modern techniques, grouped into two high-level categories: transformer models that perform reranking in multi-stage architectures and dense retrieval techniques that perform ranking directly. Two themes pervade the book: techniques for handling long documents, beyond typical sentence-by-sentence processing in NLP, and techniques for addressing the tradeoff between effectiveness (i.e., result quality) and efficiency (e.g., query latency, model and index size). Although transformer architectures and pretraining techniques are recent innovations, many aspects of how they are applied to text ranking are relatively well understood and represent mature techniques. However, there remain many open research questions, and thus in addition to laying out the foundations of pretrained transformers for text ranking, this book also attempts to prognosticate where the field is heading.
988			_aSynthesis Collection of Technology_2022
650		7	_2embne _9141188 _aProceso de textos
650		7	_2embne _9147823 _aRecuperación de la información
700	1		_aNogueira, Rodrigo _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687458
700	1		_aYates, Andrew _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
776	0	8	_iPrinted edition: _z9783031001925
776	0	8	_iPrinted edition: _z9783031010538
776	0	8	_iPrinted edition: _z9783031033094
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02181-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _esc _zSI