How a Semi‑Supervised Unified Framework Boosts E‑commerce Query Intent Classification

The paper introduces a semi‑supervised, extensible unified framework (SSUF) that integrates knowledge, label, and structural enhancements to overcome data sparsity, label bias, and fragmented sub‑tasks in e‑commerce query intent prediction, achieving superior offline and online performance.

BERTGCNSemi-supervised Learning

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How a Semi‑Supervised Unified Framework Boosts E‑commerce Query Intent Classification

Baobao Algorithm Notes

Jan 14, 2022 · Artificial Intelligence

Boosting BERT Text Classification with Label Embedding: How It Works

The paper proposes a simple yet effective method that fuses label embeddings into BERT, enhancing text‑classification performance without increasing computational cost, and validates the approach across six benchmark datasets, also exploring tf‑idf‑based label augmentation and the impact of using [SEP] versus no‑[SEP] inputs.

BERTNLPTF-IDF

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Boosting BERT Text Classification with Label Embedding: How It Works