mlsanityData to 7 Oct 2026

Word Representations via Gaussian Embedding

arXiv 1412.6623 ↗First posted 20 Dec 2014cs.CL cs.LGcited by 96

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Abstract (arXiv)

Current work in lexical distributed representations maps each word to a point vector in low-dimensional space. Mapping instead to a density provides many interesting advantages, including better capturing uncertainty about a representation and its relationships, expressing asymmetries more naturally than dot product or cosine similarity, and enabling more expressive parameterization of decision boundaries. This paper advocates for density-based distributed embeddings and presents a method for learning representations in the space of Gaussian distributions. We compare performance on various word embedding benchmarks, investigate the ability of these embeddings to model entailment and other asymmetric relationships, and explore novel properties of the representation.

Key facts. Cited by 96 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Dec 2014.

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Citing papers per year: 2021: 21; 2022: 19; 2023: 22; 2024: 16; 2025: 13; 2026: 5.

96 papers.

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  1. 2021Probabilistic Embeddings for Cross-Modal Retrievalcited by 150
  2. 2021CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learningcited by 64
  3. 2023Uncertainty in Natural Language Processing: Sources, Quantification, and Applicationscited by 44
  4. 2022MAP: Multimodal Uncertainty-Aware Vision-Language Pre-training Modelcited by 41
  5. 2022Knowledge Graph Embedding: A Survey from the Perspective of Representation Spacescited by 41
  6. 2022Sequential Recommendation via Stochastic Self-Attentioncited by 37
  7. 2021Modeling Fine-Grained Entity Types with Box Embeddingscited by 36
  8. 2021Probabilistic Modeling of Semantic Ambiguity for Scene Graph Generationcited by 35
  9. 2022Probabilistic Representations for Video Contrastive Learningcited by 33
  10. 2022Improving Cross-Modal Retrieval with Set of Diverse Embeddingscited by 33
  11. 2024ExACT: Language-guided Conceptual Reasoning and Uncertainty Estimation for Event-based Action Recognition and Morecited by 33
  12. 2022TaxoCom: Topic Taxonomy Completion with Hierarchical Discovery of Novel Topic Clusterscited by 30
38 more
  1. 2021Probabilistic Entity Representation Model for Reasoning over Knowledge Graphscited by 28
  2. 2021Modeling Sequences as Distributions with Uncertainty for Sequential Recommendationcited by 22
  3. 2022FALCON: Fast Visual Concept Learning by Integrating Images, Linguistic descriptions, and Conceptual Relationscited by 20
  4. 2023Probabilistic Prompt Learning for Dense Predictioncited by 17
  5. 2024Robust Multimodal Learning via Representation Decouplingcited by 17
  6. 2021Unsupervised Abstractive Opinion Summarization by Generating Sentences with Tree-Structured Topic Guidancecited by 14
  7. 2024RICA2: Rubric-Informed, Calibrated Assessment of Actionscited by 13
  8. 2021Word2Box: Capturing Set-Theoretic Semantics of Words using Box Embeddingscited by 11
  9. 2021Probing as Quantifying Inductive Biascited by 11
  10. 2024Improving Visual Recognition with Hyperbolical Visual Hierarchy Mappingcited by 11
  11. 2022A Rotated Hyperbolic Wrapped Normal Distribution for Hierarchical Representation Learningcited by 10
  12. 2023Can Word Sense Distribution Detect Semantic Changes of Words?cited by 10
  13. 2025Modeling Annotator Disagreement with Demographic-Aware Experts and Synthetic Perspectivescited by 10
  14. 2021Directed Graph Embeddings in Pseudo-Riemannian Manifoldscited by 9
  15. 2021Box Embeddings: An open-source library for representation learning using geometric structurescited by 9
  16. 2021TopicNet: Semantic Graph-Guided Topic Discoverycited by 9
  17. 2023Bayesian Metric Learning for Uncertainty Quantification in Image Retrievalcited by 9
  18. 2023Geometric Relational Embeddings: A Surveycited by 8
  19. 2023Unsupervised Semantic Variation Prediction using the Distribution of Sibling Embeddingscited by 8
  20. 2024Trustworthy Multimodal Fusion for Sentiment Analysis in Ordinal Sentiment Spacecited by 8
  21. 2023Introspective Deep Metric Learningcited by 7
  22. 2024When is an Embedding Model More Promising than Another?cited by 7
  23. 2025PHyCLIP: ℓ1-Product of Hyperbolic Factors Unifies Hierarchy and Compositionality in Vision-Language Representation Learningcited by 7
  24. 2024Uncertainty-aware sign language video retrieval with probability distribution modelingcited by 6
  25. 2021Quantum Mathematics in Artificial Intelligencecited by 5
  26. 2021Shades of confusion: Lexical uncertainty modulates ad hoc coordination in an interactive communication taskcited by 5
  27. 2023Personalised Federated Learning On Heterogeneous Feature Spacescited by 5
  28. 2025Geometrical Properties of Text Token Embeddings for Strong Semantic Binding in Text-to-Image Generationcited by 5
  29. 2025Language Models Are Implicitly Continuouscited by 4
  30. 2025Hierarchical Retrieval: The Geometry and a Pretrain-Finetune Recipecited by 4
  31. 2021Extending Multi-Sense Word Embedding to Phrases and Sentences for Unsupervised Semantic Applicationscited by 3
  32. 2022The Conceptual VAEcited by 3
  33. 2022Word Embedding for Social Sciences: An Interdisciplinary Surveycited by 3
  34. 2023Mutual Wasserstein Discrepancy Minimization for Sequential Recommendationcited by 3
  35. 2023Multivariate Representation Learning for Information Retrievalcited by 3
  36. 2023Training-Free Uncertainty Estimation for Embedding Modelscited by 3
  37. 2023Representation Learning via Variational Bayesian Networkscited by 3
  38. 2023Structured Probabilistic Codingcited by 3

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