Event2Vec: A Geometric Approach to Learning Composable Representations of Event Sequences

Antonin Sulc

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arXiv · 2509.12188

Event2Vec: A Geometric Approach to Learning Composable Representations of Event Sequences

Antonin Sulc

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Abstract

The study of neural representations, both in biological and artificial systems, is increasingly revealing the importance of geometric and topological structures. Inspired by this, we introduce Event2Vec, a novel framework for learning representations of discrete event sequences. Our model leverages a simple, additive recurrent structure to learn composable, interpretable embeddings. We provide a theoretical analysis demonstrating that, under specific training objectives, our model's learned representations in a Euclidean space converge to an ideal additive structure. This ensures that the representation of a sequence is the vector sum of its constituent events, a property we term the linear additive hypothesis. To address the limitations of Euclidean geometry for hierarchical data, we also introduce a variant of our model in hyperbolic space, which is naturally suited to embedding tree-like structures with low distortion. We present experiments to validate our hypothesis. Quantitative evaluation on the Brown Corpus yields a Silhouette score of 0.0564, outperforming a Word2Vec baseline (0.0215), demonstrating the model's ability to capture structural dependencies without supervision.

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BibTeX RIS

Antonin Sulc. 2025-11-30. Event2Vec: A Geometric Approach to Learning Composable Representations of Event Sequences. https://arxiv.org/abs/2509.12188

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