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

languid

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.

凝視

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.

palette

Allergic

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modest

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