Dependency-Based Word Embeddings

While continuous word embeddings are gaining popularity, current models are based solely on linear contexts. In this work, we generalize the skip-gram model with negative sampling introduced by Mikolov et al. to include arbitrary contexts. In particular, we perform experiments with dependency-based contexts, and show that they produce markedly different embeddings. The dependency-based embeddings are less topical and exhibit more functional similarity than the original skip-gram embeddings.