9789811656248 - low resource social media text mining di palakodety, shriphani; khudabukhsh, ashiqur r.; jayachandran, guha (15 risultati)
Lingua: Inglese
Editore: Springer 2021-10, 2021
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Low Resource Social Media Text Mining (SpringerBriefs in Computer Science)
Palakodety, Shriphani; KhudaBukhsh, Ashiqur R.; Jayachandran, Guha
Lingua: Inglese
Editore: Springer, 2021
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Low Resource Social Media Text Mining (SpringerBriefs in Computer Science)
Palakodety, Shriphani; KhudaBukhsh, Ashiqur R.; Jayachandran, Guha
Lingua: Inglese
Editore: Springer, 2021
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Low Resource Social Media Text Mining
Palakodety, Shriphani/ Khudabukhsh, Ashiqur R./ Jayachandran, Guha
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Editore: Springer-Nature New York Inc, 2021
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Lingua: Inglese
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Taschenbuch. Condizione: Neu. Low Resource Social Media Text Mining | Shriphani Palakodety (u. a.) | Taschenbuch | xi | Englisch | 2021 | Springer | EAN 9789811656248 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Lingua: Inglese
Editore: Springer, Springer, 2021
Serie: SpringerBriefs in Computer Science, Libro 321 di 322. Libro 321 di 322 - SpringerBriefs in Computer Science
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Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This 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 conte…nt 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.
Lingua: Inglese
Editore: Springer, 2021
Serie: SpringerBriefs in Computer Science, Libro 321 di 322. Libro 321 di 322 - SpringerBriefs in Computer Science
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Low Resource Social Media Text Mining
Palakodety, Shriphani; KhudaBukhsh, Ashiqur R.; Jayachandran, Guha
Lingua: Inglese
Editore: Springer, 2021
Serie: SpringerBriefs in Computer Science, Libro 321 di 322. Libro 321 di 322 - SpringerBriefs in Computer Science
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Lingua: Inglese
Editore: Springer Nature Singapore Okt 2021, 2021
Serie: SpringerBriefs in Computer Science, Libro 321 di 322. Libro 321 di 322 - SpringerBriefs in Computer Science
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This 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-gene…rated 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. 72 pp. Englisch.
Low Resource Social Media Text Mining (SpringerBriefs in Computer Science)
Palakodety, Shriphani; KhudaBukhsh, Ashiqur R.; Jayachandran, Guha
Lingua: Inglese
Editore: Springer, 2021
Serie: SpringerBriefs in Computer Science, Libro 321 di 322. Libro 321 di 322 - SpringerBriefs in Computer Science
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Condizione: New. Print on Demand.
Low Resource Social Media Text Mining (SpringerBriefs in Computer Science)
Palakodety, Shriphani; KhudaBukhsh, Ashiqur R.; Jayachandran, Guha
Lingua: Inglese
Editore: Springer, 2021
Serie: SpringerBriefs in Computer Science, Libro 321 di 322. Libro 321 di 322 - SpringerBriefs in Computer Science
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Low Resource Social Media Text Mining
Palakodety, Shriphani|KhudaBukhsh, Ashiqur R.|Jayachandran, Guha
Lingua: Inglese
Editore: Springer, Berlin|Springer Nature Singapore|Springer, 2021
Serie: SpringerBriefs in Computer Science, Libro 321 di 322. Libro 321 di 322 - SpringerBriefs in Computer Science
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Da: moluna, Greven, Germaniamoluna
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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-m.
Lingua: Inglese
Editore: Springer, Springer Okt 2021, 2021
Serie: SpringerBriefs in Computer Science, Libro 321 di 322. Libro 321 di 322 - SpringerBriefs in Computer Science
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This 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-generate…d 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 72 pp. Englisch.
