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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Low Resource Social Media Text Mining

Low Resource Social Media Text Mining

Palakodety, Shriphani
Khudabukhsh, Ashiqur R.
Jayachandran, Guha

Paperback

Series: Springerbriefs in Computer Science

General ComputersProbability & Statistics

ISBN10: 981165624X
ISBN13: 9789811656248
Publisher: Springer
Published: Oct 3 2021
Pages: 60
Weight: 0.26
Height: 0.15 Width: 6.14 Depth: 9.21
Language: English

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 languageskill levels. Resources like corpora and models are also scarce. Due to these factors, newer methods are needed to process such text.

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