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 dependencybased embeddings are less topical and exhibit more functional similarity than the original skip gram embeddings.
ACL