Abstract
Word embeddings seek to recover a Euclidean metric space by mapping words into vectors, starting from words co-occurrences in a corpus. Word embeddings may underestimate the similarity between nearby words, and overestimate it between distant words in the Euclidean metric space. In this paper, we re-embed pre-trained word embeddings with a stage of manifold learning which retains dimensionality. We show that this approach is theoretically founded in the metric recovery paradigm, and empirically show that it can improve on state-of-the-art embeddings in word similarity tasks 0.5 - 5.0% points depending on the original space.- Anthology ID:
- D17-1033
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 321–326
- Language:
- URL:
- https://aclanthology.org/D17-1033
- DOI:
- 10.18653/v1/D17-1033
- Cite (ACL):
- Souleiman Hasan and Edward Curry. 2017. Word Re-Embedding via Manifold Dimensionality Retention. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 321–326, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Word Re-Embedding via Manifold Dimensionality Retention (Hasan & Curry, EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/D17-1033.pdf