Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings

2021-04-18 · Source: stat.ML updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

A novel two-stage estimator is proposed for efficiently transfer learning domain-specific word embeddings, addressing the challenge of adapting word meanings in new domains with limited data. This method, based on group-sparse matrix factorization, combines large-scale text corpora like Wikipedia with smaller, domain-specific datasets. It is particularly effective when only a small number of word embeddings require alteration between domains, such as "positive" having a negative connotation in medical notes. The estimator is proven to achieve high accuracy with substantially less domain-specific data under these conditions. Furthermore, the research demonstrates that all local minima of its nonconvex objective function are statistically indistinguishable from the global minimum, ensuring efficient computation. This work also establishes the first bounds on group-sparse matrix factorization.

Key takeaway

For NLP engineers adapting pre-trained word embeddings to specialized domains, this group-sparse matrix factorization approach offers a robust solution. You can achieve high accuracy with substantially less domain-specific data, particularly when only a few word meanings shift. Consider integrating this two-stage estimator to efficiently refine embeddings for tasks like sentiment analysis in healthcare, reducing the need for extensive domain-specific corpus collection.

Key insights

Group-sparse matrix factorization efficiently transfers word embeddings by combining general and domain-specific data, especially when few words change meaning.

Principles

• Domain-specific word meanings often differ minimally.
• Group-sparse penalties enable efficient transfer learning.
• Local minima can be globally optimal in nonconvex objectives.

Method

A two-stage estimator applies group-sparse matrix factorization to combine large-scale general text with limited domain-specific data for efficient word embedding transfer.

In practice

• Adapt word embeddings for specialized text analysis.
• Reduce data needs for domain-specific NLP tasks.
• Improve sentiment analysis in medical notes.

Topics

• Group-Sparse Matrix Factorization
• Word Embeddings
• Transfer Learning
• Natural Language Processing
• Domain Adaptation
• Unsupervised Learning

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by stat.ML updates on arXiv.org.