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008			220601s2020 sz | s |||| 0|eng d
020			_a9783031021749
024	7		_a10.1007/978-3-031-02174-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.N38 _b2020 EB
100	1		_aDror, Rotem _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687991
245	1	0	_aStatistical Significance Testing for Natural Language Processing _cby Rotem Dror, Lotem Peled-Cohen, Segev Shlomov, Roi Reichart
250			_a1st edition 2020
264		1	_aCham _bSpringer International Publishing _c2020
300			_a1 recurso en línea (XVII, 98 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 -- Statistical Hypothesis Testing -- Statistical Significance Tests -- Statistical Significance in NLP -- Deep Significance -- Replicability Analysis -- Open Questions and Challenges -- Conclusions -- Bibliography -- Authors' Biographies.
520			_aData-driven experimental analysis has become the main evaluation tool of Natural Language Processing (NLP) algorithms. In fact, in the last decade, it has become rare to see an NLP paper, particularly one that proposes a new algorithm, that does not include extensive experimental analysis, and the number of involved tasks, datasets, domains, and languages is constantly growing. This emphasis on empirical results highlights the role of statistical significance testing in NLP research: If we, as a community, rely on empirical evaluation to validate our hypotheses and reveal the correct language processing mechanisms, we better be sure that our results are not coincidental. The goal of this book is to discuss the main aspects of statistical significance testing in NLP. Our guiding assumption throughout the book is that the basic question NLP researchers and engineers deal with is whether or not one algorithm can be considered better than another one. This question drives the field forward as it allows the constant progress of developing better technology for language processing challenges. In practice, researchers and engineers would like to draw the right conclusion from a limited set of experiments, and this conclusion should hold for other experiments with datasets they do not have at their disposal or that they cannot perform due to limited time and resources. The book hence discusses the opportunities and challenges in using statistical significance testing in NLP, from the point of view of experimental comparison between two algorithms. We cover topics such as choosing an appropriate significance test for the major NLP tasks, dealing with the unique aspects of significance testing for non-convex deep neural networks, accounting for a large number of comparisons between two NLP algorithms in a statistically valid manner (multiple hypothesis testing), and, finally, the unique challenges yielded by the nature of the data and practices of the field.
988			_aSynthesis Collection of Technology_2020
650		7	_2embne _9687996 _aContrastación de hipótesis (Estadística)
650		7	_2embne _9158738 _aProceso en lenguaje natural (Informática)
700	1		_aPeled-Cohen, Lotem _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687992
700	1		_aShlomov, Segev _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687993
700	1		_aReichart, Roi, _d1980- _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687994
776	0	8	_iPrinted edition: _z9783031001857
776	0	8	_iPrinted edition: _z9783031010460
776	0	8	_iPrinted edition: _z9783031033025
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02174-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b04/2023 _dz _esc _zSI