Titlebook: Low Resource Social Media Text Mining; Shriphani Palakodety,Ashiqur R. KhudaBukhsh,Guha J Book 2021 The Author(s), under exclusive license

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The Problem Setting, This chapter will help NLP practitioners understand the importance of analyzing the low-resource components of corpora from various societies and how ignoring them can skew results, how to go about addressing these, and a broad set of examples and statistics to reinforce the importance of low-resource social media text mining.

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Semantic Sampling,discussed allow task-specific data sets and models to be constructed rapidly often using just a handful of initial samples. We then explore extensions to sample across languages—allowing powerful pipelines that can transfer resources from well-resourced languages to their low-resource counterparts.

膽小懦夫

A Rapid Tour of NLP,309 (2017)?[.]) which provide a thorough and rigorous grounding of NLP. We discuss static and contextual word and document embeddings, and their applications. We then look at polyglot training in static and contextual embeddings.

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Allergic

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The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl