# Mittens: An Extension of GloVe for Learning Domain-Specialized Representations

arXiv: https://arxiv.org/abs/1803.09901 · HTML: https://mlsanity.com/paper/1803.09901/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2018.

## Built on by (most cited first, 8 of 8)

- [Capitalization and Punctuation Restoration: a Survey](https://mlsanity.com/paper/2111.10746/) (2021)
- [Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings](https://mlsanity.com/paper/2104.08928/) (2021)
- [Automated Refugee Case Analysis: An NLP Pipeline for Supporting Legal Practitioners](https://mlsanity.com/paper/2305.15533/) (2023)
- [DANES: Deep Neural Network Ensemble Architecture for Social and Textual Context-aware Fake News Detection](https://mlsanity.com/paper/2302.01756/) (2023)
- [Empowering Refugee Claimants and their Lawyers: Using Machine Learning to Examine Decision-Making in Refugee Law](https://mlsanity.com/paper/2308.11531/) (2023)
- [A Comparison of Approaches for Imbalanced Classification Problems in the Context of Retrieving Relevant Documents for an Analysis](https://mlsanity.com/paper/2205.01600/) (2022)
- [Word Embeddings for Banking Industry](https://mlsanity.com/paper/2306.01807/) (2023)
- [LGDE: Local Graph-based Dictionary Expansion](https://mlsanity.com/paper/2405.07764/) (2024)

## Abstract (arXiv)

We present a simple extension of the GloVe representation learning model that begins with general-purpose representations and updates them based on data from a specialized domain. We show that the resulting representations can lead to faster learning and better results on a variety of tasks.