Word Tour: One-dimensional Word Embeddings via the Traveling Salesman Problem

Ryoma Sato


Abstract
Word embeddings are one of the most fundamental technologies used in natural language processing. Existing word embeddings are high-dimensional and consume considerable computational resources. In this study, we propose WordTour, unsupervised one-dimensional word embeddings. To achieve the challenging goal, we propose a decomposition of the desiderata of word embeddings into two parts, completeness and soundness, and focus on soundness in this paper. Owing to the single dimensionality, WordTour is extremely efficient and provides a minimal means to handle word embeddings. We experimentally confirmed the effectiveness of the proposed method via user study and document classification.
Anthology ID:
2022.naacl-main.157
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2166–2172
Language:
URL:
https://aclanthology.org/2022.naacl-main.157
DOI:
10.18653/v1/2022.naacl-main.157
Bibkey:
Cite (ACL):
Ryoma Sato. 2022. Word Tour: One-dimensional Word Embeddings via the Traveling Salesman Problem. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2166–2172, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Word Tour: One-dimensional Word Embeddings via the Traveling Salesman Problem (Sato, NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.157.pdf
Software:
 2022.naacl-main.157.software.zip
Code
 joisino/wordtour