Dependency-Based Word Embeddings

Citations Over TimeTop 1% of 2014 papers

Abstract

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.

Related Papers

• → Dependency Forest for Sentiment Analysis(2012)13 cited
• High Quality Dependency Selection from Automatic Parses(2013)
• → Factors influencing dependency parsing of coordinating structure(2009)1 cited
• → A Simulated Shallow Dependency Parser Based on Weighted Hierarchical Structure Learning(2008)1 cited
• → A simulated shallow dependency parser based on weighted hierarchical structure learning(2008)