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020			_a9789811656255
024	7		_a10.1007/978-981-16-5625-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ334 _b2021 EB
100			_aPalakodety, Shriphani _1https://orcid.org/0000-0001-9966-9422 _9681084 _cLow Resource Social Media Text Mining
245	1	0	_aLow Resource Social Media Text Mining _cby Shriphani Palakodety, Ashiqur R. KhudaBukhsh, Guha Jayachandran.
250			_aFirst edition 2021
264		1	_aSingapore _bSpringer International Publising _bSpringer International Publising _c2021.
300			_a1 recurso en línea (XI, 60 páginas) _b14 ilustraciones, 8 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aSpringerBriefs in Computer Science _x2191-5776
490	0		_aComputer Science (SpringerNature-11645)
490	0		_aComputer Science (R0) (SpringerNature-43710)
505	0		_aChapter 1: Introduction and outline -- Chapter 2: Natural Language Processing Preliminary -- Chapter 3: Low-Resource Multilingual Social Media Text and Challenges -- Chapter4: Robust Language Identification -- Chapter 5: Semantic Sampling -- Chapter6: Unsupervised Machine Translation.
520	3		_aThis book focuses on methods that are unsupervised or require minimal supervision-vital in the low-resource domain. Over the past few years, rapid growth in Internet access across the globe has resulted in an explosion in user-generated text content in social media platforms. This effect is significantly pronounced in linguistically diverse areas of the world like South Asia, where over 400 million people regularly access social media platforms. YouTube, Facebook, and Twitter report a monthly active user base in excess of 200 million from this region. Natural language processing (NLP) research and publicly available resources such as models and corpora prioritize Web content authored primarily by a Western user base. Such content is authored in English by a user base fluent in the language and can be processed by a broad range of off-the-shelf NLP tools. In contrast, text from linguistically diverse regions features high levels of multilinguality, code-switching, and varied language skill levels. Resources like corpora and models are also scarce. Due to these factors, newer methods are needed to process such text. This book is designed for NLP practitioners well versed in recent advances in the field but unfamiliar with the landscape of low-resource multilingual NLP. The contents of this book introduce the various challenges associated with social media content, quantify these issues, and provide solutions and intuition. When possible, the methods discussed are evaluated on real-world social media data sets to emphasize their robustness to the noisy nature of the social media environment. On completion of the book, the reader will be well-versed with the complexity of text-mining in multilingual, low-resource environments; will be aware of a broad set of off-the-shelf tools that can be applied to various problems; and will be able to conduct sophisticated analyses of such text.
988			_aSpringer_Computer_2021
650		7	_2embne _aInteligencia artificial _9413115
700	1		_aKhudaBukhsh, Ashiqur R _eautor _1https://orcid.org/0000-0003-2394-7902
700	1		_aJayachandran, Guha _eautor
776	0	8	_iPrinted edition: _z9789811656248
776	0	8	_iPrinted edition: _z9789811656262
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-16-5625-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2022 _dz _eh _zSI