Word Tour: One-dimensional Word Embeddings via the Traveling Salesman Problem
This addresses the need for more efficient word embeddings in natural language processing, though it appears incremental as it focuses on reducing dimensionality rather than a fundamental breakthrough.
The authors tackled the problem of high computational resource consumption in existing high-dimensional word embeddings by proposing WordTour, unsupervised one-dimensional word embeddings, which they experimentally confirmed as effective via user study and document classification.
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.