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Semantic Sampling

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Low Resource Social Media Text Mining

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

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Abstract

A variety of tasks involving social media text require mining rare samples. In text classification, information retrieval, and other NLP tasks, working with very skewed or imbalanced data sets poses many challenges. In such settings, training data sets can be rapidly bootstrapped using highly targeted sampling strategies. This chapter draws on work in active learning, semantic similarity, and sampling strategies to address a variety of social media text mining tasks. The topics involved are particularly well suited for social media analysis. Most tasks surrounding user generated social media text such as content moderation, and recommendations often involve rapid model construction in response to real world events in real time. The methods 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.

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Notes

  1. 1.

    Example from Khuda Bukhsh et al. [17].

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Author information

Authors and Affiliations

  1. Onai Inc., San Jose, CA, USA

    Shriphani Palakodety

  2. Golisano College of Computing and Information Sciences, Rochester Institute of Technology, Rochester, NY, USA

    Ashiqur R. KhudaBukhsh

  3. Onai Inc., San Jose, CA, USA

    Guha Jayachandran

Authors
  1. Shriphani Palakodety
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  2. Ashiqur R. KhudaBukhsh
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  3. Guha Jayachandran
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Corresponding author

Correspondence to Shriphani Palakodety .

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Palakodety, S., KhudaBukhsh, A.R., Jayachandran, G. (2021). Semantic Sampling. In: Low Resource Social Media Text Mining. SpringerBriefs in Computer Science. Springer, Singapore. https://doi.org/10.1007/978-981-16-5625-5_6

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  • DOI: https://doi.org/10.1007/978-981-16-5625-5_6

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