Semi-supervised Dependency Parsing using Bilexical Contextual Features from Auto-Parsed Data


We present a semi-supervised approach to improve dependency parsing accuracy by using bilexical statistics derived from auto-parsed data. The method is based on estimating the attachment potential of head-modifier words, by taking into account not only the head and modifier words themselves, but also the words surrounding the head and the modifier. When integrating the learned statistics as features in a graph-based parsing model, we observe nice improvements in accuracy when parsing various English datasets.