000 04460nam a22005415i 4500
001 978-3-031-02175-6
003 DE-He213
005 20240730164404.0
007 cr nn 008mamaa
008 220601s2020 sz | s |||| 0|eng d
020 _a9783031021756
_9978-3-031-02175-6
024 7 _a10.1007/978-3-031-02175-6
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aFarzindar, Anna Atefeh.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_984242
245 1 0 _aNatural Language Processing for Social Media, Third Edition
_h[electronic resource] /
_cby Anna Atefeh Farzindar, Diana Inkpen.
250 _a3rd ed. 2020.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2020.
300 _aXXV, 193 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Human Language Technologies,
_x1947-4059
505 0 _aList of Figures -- List of Tables -- Preface -- 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 -- Index.
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 that extract relevant information from a large volume of data coming from multiple sources and languages in various formats or in free form. This book will 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 NLP tools and methods for processing the non-traditional information from social media data that is available in large amounts, and it shows how innovative NLP approaches can integrate appropriate linguistic information in various fields such as social media monitoring, health care, and business intelligence. The book further covers 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), the National Institute of Standards and Technology via the Text REtrieval Conference (TREC) and the Text Analysis Conference (TAC), or the Conference and Labs of the Evaluation Forum (CLEF). In this third edition of the book, the authors added information about recent progress in NLP for social media applications, including more about the modern techniques provided by deep neural networks (DNNs) for modeling language and analyzing social media data.
650 0 _aArtificial intelligence.
_93407
650 0 _aNatural language processing (Computer science).
_94741
650 0 _aComputational linguistics.
_96146
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aNatural Language Processing (NLP).
_931587
650 2 4 _aComputational Linguistics.
_96146
700 1 _aInkpen, Diana.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_984246
710 2 _aSpringerLink (Online service)
_984247
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031001864
776 0 8 _iPrinted edition:
_z9783031010477
776 0 8 _iPrinted edition:
_z9783031033032
830 0 _aSynthesis Lectures on Human Language Technologies,
_x1947-4059
_984248
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02175-6
912 _aZDB-2-SXSC
942 _cEBK
999 _c85637
_d85637