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Ensembled Transferred Embeddings

In: Machine Learning for Data Science Handbook

Author

Listed:
  • Yonatan Hadar

    (Tel Aviv University, Department of Industrial Engineering)

  • Erez Shmueli

    (Tel Aviv University, Department of Industrial Engineering)

Abstract

Deep learning has become a very popular method for text classification in recent years, due to its ability to improve the accuracy of previous state-of-the-art methods on several benchmarks. However, these improvements required hundreds of thousands to millions labeled training examples, which in many cases can be very time consuming and/or expensive to acquire. This problem is especially significant in domain specific text classification tasks where pretrained embeddings and models are not optimal. In order to cope with this problem, we propose a novel learning framework, Ensembled Transferred Embeddings (ETE), which relies on two key ideas: (1) Labeling a relatively small sample of the target dataset, in a semi-automatic process (2) Leveraging other datasets from related domains or related tasks that are large-scale and labeled, to extract “transferable embeddings” Evaluation of ETE on a large-scale real-world item categorization dataset provided to us by PayPal, shows that it significantly outperforms traditional as well as state-of-the-art item categorization methods.

Suggested Citation

  • Yonatan Hadar & Erez Shmueli, 2023. "Ensembled Transferred Embeddings," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 587-606, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-24628-9_26
    DOI: 10.1007/978-3-031-24628-9_26
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