mlsanityData to 14 Oct 2026

A Mixture Model for Learning Multi-Sense Word Embeddings

arXiv 1706.05111 ↗First posted 15 Jun 2017cs.CLcited by 12

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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.

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

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Citing papers per year: 2021: 3; 2022: 2; 2023: 3; 2024: 4.

12 papers.

  1. 2021Probabilistic Embeddings for Cross-Modal Retrievalcited by 151
  2. 2023Improved Probabilistic Image-Text Representationscited by 40
  3. 2024Probabilistic Language-Image Pre-Trainingcited by 16
  4. 2023Driving Context into Text-to-Text Privatizationcited by 13
  5. 2023Introspective Deep Metric Learningcited by 7
  6. 2024Domain Generalization with Small Datacited by 6
  7. 2021A Survey On Neural Word Embeddingscited by 2
  8. 2022Probabilistic Deep Metric Learning for Hyperspectral Image Classificationcited by 2
  9. 2021EDS-MEMBED: Multi-sense embeddings based on enhanced distributional semantic structures via a graph walk over word sensescited by 1
  10. 2024pEBR: A Probabilistic Approach to Embedding Based Retrievalcited by 1
  11. 2022Introspective Deep Metric Learning for Image Retrievalcited by 0
  12. 2024Information Cascade Prediction under Public Emergencies: A Surveycited by 0