# Learning Word Representations with Hierarchical Sparse Coding

arXiv: https://arxiv.org/abs/1406.2035 · HTML: https://mlsanity.com/paper/1406.2035/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2014.

## Built on by (most cited first, 4 of 4)

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- [BLCU-ICALL at SemEval-2022 Task 1: Cross-Attention Multitasking Framework for Definition Modeling](https://mlsanity.com/paper/2204.07701/) (2022)
- [HFT-ONLSTM: Hierarchical and Fine-Tuning Multi-label Text Classification](https://mlsanity.com/paper/2204.08115/) (2022)
- [Emergent organization of receptive fields in networks of excitatory and inhibitory neurons](https://mlsanity.com/paper/2205.13614/) (2022)

## Abstract (arXiv)

We propose a new method for learning word representations using hierarchical regularization in sparse coding inspired by the linguistic study of word meanings. We show an efficient learning algorithm based on stochastic proximal methods that is significantly faster than previous approaches, making it possible to perform hierarchical sparse coding on a corpus of billions of word tokens. Experiments on various benchmark tasks---word similarity ranking, analogies, sentence completion, and sentiment analysis---demonstrate that the method outperforms or is competitive with state-of-the-art methods. Our word representations are available at urlhttp://www.ark.cs.cmu.edu/dyogatam/wordvecs/.