Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings

Short summary

A novel two-stage estimator with a group-sparse penalty efficiently transfers word embeddings to new domains using limited data, achieving high accuracy even when only a few word meanings change.

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