Word Tour: One-dimensional Word Embeddings via the Traveling Salesman Problem
Ryoma Sato · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · 2022
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 onedimensional 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.