Multiple weak supervision for short text classification

Li-Ming Chen

doi:10.1007/s10489-021-02958-3

Multiple weak supervision for short text classification

2022-01-01

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AI

DOI：10.1007/s10489-021-02958-3

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For short text classification, insufficient labeled data, data sparsity, and imbalanced classification have become three major challenges. For this, we proposed multiple weak supervision, which can label unlabeled data automatically. Different from prior work, the proposed method can generate probabilistic labels through conditional independent model. What's more, experiments were conducted to verify the effectiveness of multiple weak supervision. According to experimental results on public dadasets, real datasets and synthetic datasets, unlabeled imbalanced short text classification problem can be solved effectively by multiple weak supervision. Notably, without reducing precision, recall, and F1-score can be improved by adding distant supervision clustering, which can be used to meet different application needs.

Keywords:

Multiple weak supervision

Short text classification

Imbalanced classification

Distant supervision clustering

Probabilistic labels

Multiple weak supervision for short text classification

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