Abstract

We introduce EigenNoise, a novel initialization scheme for word vectors based on dense co-occurrence modeling. Our approach achieves competitive performance with GloVe without requiring extensive pre-training data, providing an efficient alternative for scenarios with limited computational resources or data availability.

Key Contributions

  • Dense Co-occurrence Modeling: Novel approach to capturing word relationships through dense matrix representations
  • Efficient Initialization: Warm-start method that reduces training time and data requirements
  • Competitive Performance: Achieves results comparable to GloVe with significantly less computational overhead
  • Theoretical Foundation: Principled approach based on contrastive learning principles

Technical Innovation

EigenNoise leverages eigenvalue decomposition of co-occurrence matrices to create informed initializations for word embeddings. This approach combines the theoretical rigor of matrix factorization methods with the practical benefits of neural embedding approaches.

Impact

This work addresses a practical challenge in NLP applications where full-scale pre-training is not feasible, providing researchers and practitioners with an efficient alternative for generating high-quality word representations.

Significance

The research contributes to understanding the mathematical foundations of word embeddings and offers practical solutions for resource-constrained environments, particularly relevant for specialized domains or languages with limited corpora.

Citation

@article{heidenreich2022eigennoise,
  title={EigenNoise: A Contrastive Prior to Warm-Start Representations},
  author={Heidenreich, Hunter Scott and Williams, Jake Ryland},
  journal={arXiv preprint arXiv:2205.04376},
  year={2022}
}