# A Mixture Model for Learning Multi-Sense Word Embeddings

arXiv: https://arxiv.org/abs/1706.05111 · HTML: https://mlsanity.com/paper/1706.05111/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2017.

## Built on by (most cited first, 12 of 12)

- [Probabilistic Embeddings for Cross-Modal Retrieval](https://mlsanity.com/paper/2101.05068/) (2021)
- [Improved Probabilistic Image-Text Representations](https://mlsanity.com/paper/2305.18171/) (2023)
- [Probabilistic Language-Image Pre-Training](https://mlsanity.com/paper/2410.18857/) (2024)
- [Driving Context into Text-to-Text Privatization](https://mlsanity.com/paper/2306.01457/) (2023)
- [Introspective Deep Metric Learning](https://mlsanity.com/paper/2309.09982/) (2023)
- [Domain Generalization with Small Data](https://mlsanity.com/paper/2402.06150/) (2024)
- [A Survey On Neural Word Embeddings](https://mlsanity.com/paper/2110.01804/) (2021)
- [Probabilistic Deep Metric Learning for Hyperspectral Image Classification](https://mlsanity.com/paper/2211.08349/) (2022)
- [EDS-MEMBED: Multi-sense embeddings based on enhanced distributional semantic structures via a graph walk over word senses](https://mlsanity.com/paper/2103.00232/) (2021)
- [pEBR: A Probabilistic Approach to Embedding Based Retrieval](https://mlsanity.com/paper/2410.19349/) (2024)
- [Introspective Deep Metric Learning for Image Retrieval](https://mlsanity.com/paper/2205.04449/) (2022)
- [Information Cascade Prediction under Public Emergencies: A Survey](https://mlsanity.com/paper/2404.01319/) (2024)

## Abstract (arXiv)

Word embeddings are now a standard technique for inducing meaning representations for words. For getting good representations, it is important to take into account different senses of a word. In this paper, we propose a mixture model for learning multi-sense word embeddings. Our model generalizes the previous works in that it allows to induce different weights of different senses of a word. The experimental results show that our model outperforms previous models on standard evaluation tasks.