# A Simple Regularization-based Algorithm for Learning Cross-Domain Word Embeddings

arXiv: https://arxiv.org/abs/1902.00184 · HTML: https://mlsanity.com/paper/1902.00184/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2019.

## Built on by (most cited first, 3 of 3)

- [Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings](https://mlsanity.com/paper/2104.08928/) (2021)
- [Domain Adaptation for Sparse-Data Settings: What Do We Gain by Not Using Bert?](https://mlsanity.com/paper/2203.16926/) (2022)
- [Leveraging knowledge graphs to update scientific word embeddings using latent semantic imputation](https://mlsanity.com/paper/2210.15358/) (2022)

## Abstract (arXiv)

Learning word embeddings has received a significant amount of attention recently. Often, word embeddings are learned in an unsupervised manner from a large collection of text. The genre of the text typically plays an important role in the effectiveness of the resulting embeddings. How to effectively train word embedding models using data from different domains remains a problem that is underexplored. In this paper, we present a simple yet effective method for learning word embeddings based on text from different domains. We demonstrate the effectiveness of our approach through extensive experiments on various down-stream NLP tasks.