A Discriminative Approach to Tree Alignment

Abstract

In this paper we propose a discriminative framework for automatic tree alignment. We use a rich feature set and a log-linear model trained on small amounts of hand-aligned training data. We include contextual features and link dependencies to improve the results even further. We achieve an overall F-score of almost 80 % which is significantly better than other scores reported for this task. 1