000 04201nam a22005415i 4500
001 978-981-16-5625-5
003 DE-He213
005 20240423125443.0
007 cr nn 008mamaa
008 211001s2021 si | s |||| 0|eng d
020 _a9789811656255
_9978-981-16-5625-5
024 7 _a10.1007/978-981-16-5625-5
_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 _aPalakodety, Shriphani.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aLow Resource Social Media Text Mining
_h[electronic resource] /
_cby Shriphani Palakodety, Ashiqur R. KhudaBukhsh, Guha Jayachandran.
250 _a1st ed. 2021.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2021.
300 _aXI, 60 p. 14 illus., 8 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Computer Science,
_x2191-5776
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 _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 languageskill 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.
650 0 _aArtificial intelligence.
650 0 _aMachine learning.
650 1 4 _aArtificial Intelligence.
650 2 4 _aMachine Learning.
700 1 _aKhudaBukhsh, Ashiqur R.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aJayachandran, Guha.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789811656248
776 0 8 _iPrinted edition:
_z9789811656262
830 0 _aSpringerBriefs in Computer Science,
_x2191-5776
856 4 0 _uhttps://doi.org/10.1007/978-981-16-5625-5
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c178105
_d178105