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008			220601s2022 sz | o |||| 0|eng d
020			_a9783031037634
024	7		_a10.1007/978-3-031-03763-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aP98.5 .S83 _b2022 EB
100	1		_aPaun, Silviu _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687660
245	1	0	_aStatistical Methods for Annotation Analysis _cby Silviu Paun, Ron Artstein, Massimo Poesio
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XIX, 197 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 -- Acknowledgements -- Introduction -- Coefficients of Agreement -- Using Agreement Measures for CL Annotation Tasks -- Probabilistic Models of Agreement -- Probabilistic Models of Annotation -- Learning from Multi-Annotated Corpora -- Bibliography -- Authors' Biographies.
520			_aLabelling data is one of the most fundamental activities in science, and has underpinned practice, particularly in medicine, for decades, as well as research in corpus linguistics since at least the development of the Brown corpus. With the shift towards Machine Learning in Artificial Intelligence (AI), the creation of datasets to be used for training and evaluating AI systems, also known in AI as corpora, has become a central activity in the field as well. Early AI datasets were created on an ad-hoc basis to tackle specific problems. As larger and more reusable datasets were created, requiring greater investment, the need for a more systematic approach to dataset creation arose to ensure increased quality. A range of statistical methods were adopted, often but not exclusively from the medical sciences, to ensure that the labels used were not subjective, or to choose among different labels provided by the coders. A wide variety of such methods is now in regular use. This book is meant to provide a survey of the most widely used among these statistical methods supporting annotation practice. As far as the authors know, this is the first book attempting to cover the two families of methods in wider use. The first family of methods is concerned with the development of labelling schemes and, in particular, ensuring that such schemes are such that sufficient agreement can be observed among the coders. The second family includes methods developed to analyze the output of coders once the scheme has been agreed upon, particularly although not exclusively to identify the most likely label for an item among those provided by the coders. The focus of this book is primarily on Natural Language Processing, the area of AI devoted to the development of models of language interpretation and production, but many if not most of the methods discussed here are also applicable to other areas of AI, or indeed, to other areas of Data Science.
988			_aSynthesis Collection of Technology_2022
650		7	_2embne _aLingüística computacional _xMétodos estadísticos _9666075
700	1		_aArtstein, Ron _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
700			_aPoesio, Massimo _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
776	0	8	_iPrinted edition: _z9783031037733
776	0	8	_iPrinted edition: _z9783031037535
776	0	8	_iPrinted edition: _z9783031037832
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-03763-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _eb _zSI