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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 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.
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