Dependency Link Embeddings: Continuous Representations of Syntactic Substructures

Abstract

We present a simple method to learn continuous representations of dependency substructures (links), with the motivation of directly working with higher-order, structured embeddings and their hidden relationships, and also to avoid the millions of sparse, template-based word-cluster features in dependency parsing. These link embeddings allow a significantly smaller and simpler set of unary features for dependency parsing, while maintaining improvements similar to state-of-the-art, n-ary word-cluster features, and also stacking over them. Moreover, these link vectors (made publicly available) are directly portable as offthe-shelf, dense, syntactic features in various NLP tasks. As one example, we incorporate them into constituent parse reranking, where their small feature set again matches the performance of standard non-local, manuallydefined features, and also stacks over them.