Distributional Models and Deep Learning Embeddings: Combining the Best of Both Worlds
Abstract
There are two main approaches to the distributed representation of words: low-dimensional deep learning embeddings and high-dimensional distributional models, in which each dimension corresponds to a context word. In this paper, we combine these two approaches by learning embeddings based on distributional-model vectors - as opposed to one-hot vectors as is standardly done in deep learning. We show that the combined approach has better performance on a word relatedness judgment task.
Cite
Text
Sergienya and Schütze. "Distributional Models and Deep Learning Embeddings: Combining the Best of Both Worlds." International Conference on Learning Representations, 2014.Markdown
[Sergienya and Schütze. "Distributional Models and Deep Learning Embeddings: Combining the Best of Both Worlds." International Conference on Learning Representations, 2014.](https://mlanthology.org/iclr/2014/sergienya2014iclr-distributional/)BibTeX
@inproceedings{sergienya2014iclr-distributional,
title = {{Distributional Models and Deep Learning Embeddings: Combining the Best of Both Worlds}},
author = {Sergienya, Irina and Schütze, Hinrich},
booktitle = {International Conference on Learning Representations},
year = {2014},
url = {https://mlanthology.org/iclr/2014/sergienya2014iclr-distributional/}
}