Word Embeddings as Metric Recovery in Semantic Spaces
Name
tacl_a_00098.pdf
Description
Published version
Size
1.64 MB
Format
Adobe PDF
Checksum (MD5)
78e4b158acc669f5bc3598756b909377
Author(s)
Hashimoto, Tatsunori B
Alvarez-Melis, David
Jaakkola, Tommi S
Date Issued
2016
Journal
Transactions of the Association for Computational Linguistics
Publisher
MIT Press - Journals
Version
Final published version
Abstract
Continuous word representations have been remarkably useful across NLP tasks but remain poorly understood. We ground word embeddings in semantic spaces studied in the cognitive-psychometric literature, taking these spaces as the primary objects to recover. To this end, we relate log co-occurrences of words in large corpora to semantic similarity assessments and show that co-occurrences are indeed consistent with an Euclidean semantic space hypothesis. Framing word embedding as metric recovery of a semantic space unifies existing word embedding algorithms, ties them to manifold learning, and demonstrates that existing algorithms are consistent metric recovery methods given co-occurrence counts from random walks. Furthermore, we propose a simple, principled, direct metric recovery algorithm that performs on par with the state-of-the-art word embedding and manifold learning methods. Finally, we complement recent focus on analogies by constructing two new inductive reasoning datasets—series completion and classification—and demonstrate that word embeddings can be used to solve them as well.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1162/TACL_A_00098
