000			03917nam a22004095i 4500
999			_c387101 _d387101
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003			ES-MaUEC
005			20230505141741.0
006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			230218s2015 sz | s |||| 0|eng d
020			_a9783031021572
024	7		_a10.1007/978-3-031-02157-2 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.N38 _b2015 EB
100	1		_aFarzindar, Atefeh _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686960
245	1	0	_aNatural Language Processing for Social Media _cby Atefeh Farzindar
250			_a1st edition 2015
264		1	_aCham _bSpringer International Publishing _c2015
300			_a1 recurso en línea (IV, 166 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 to Social Media Analysis -- Linguistic Pre-processing\\ of Social Media Texts -- Semantic Analysis of Social Media Texts -- Applications of Social Media Text Analysis -- Data Collection, Annotation, and Evaluation -- Conclusion and Perspectives -- Glossary -- Bibliography -- Authors' Biographies.
520			_aIn recent years, online social networking has revolutionized interpersonal communication. The newer research on language analysis in social media has been increasingly focusing on the latter's impact on our daily lives, both on a personal and a professional level. Natural language processing (NLP) is one of the most promising avenues for social media data processing. It is a scientific challenge to develop powerful methods and algorithms which extract relevant information from a large volume of data coming from multiple sources and languages in various formats or in free form. We discuss the challenges in analyzing social media texts in contrast with traditional documents. Research methods in information extraction, automatic categorization and clustering, automatic summarization and indexing, and statistical machine translation need to be adapted to a new kind of data. This book reviews the current research on Natural Language Processing (NLP) tools and methods for processing the non-traditional information from social media data that is available in large amounts (big data), and shows how innovative NLP approaches can integrate appropriate linguistic information in various fields such as social media monitoring, health care, business intelligence, industry, marketing, and security and defense. We review the existing evaluation metrics for NLP and social media applications, and the new efforts in evaluation campaigns or shared tasks on new datasets collected from social media. Such tasks are organized by the Association for Computational Linguistics (such as SemEval tasks) or by the National Institute of Standards and Technology via the Text REtrieval Conference (TREC) and the Text Analysis Conference (TAC). In the concluding chapter, we discuss the importance of this dynamic discipline and its great potential for NLP in the coming decade, in the context of changes in mobile technology, cloud computing, and social networking.
988			_aSynthesis Collection of Technology_2015
650		7	_2embne _9158738 _aProceso en lenguaje natural (Informática)
650		7	_2embne _9686197 _aMedios sociales
776	0	8	_iPrinted edition: _z9783031010293
776	0	8	_iPrinted edition: _z9783031032851
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02157-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2023 _dz _eIG _zSI