Probabilistic Parsing Action Models for Multi-Lingual Dependency Parsing

Xiangyu Duan, Jun Zhao, Bo Xu

Abstract: Deterministic dependency parsers use parsing actions to construct dependencies. These parsers do not compute the probability of the whole dependency tree. They only determine parsing actions stepwisely by a trained classifier. To globally model parsing actions of all steps that are taken on the input sentence, we propose two kinds of probabilistic parsing action models that can compute the probability of the whole dependency tree. The tree with the maximal probability is outputted. The experiments are carried on 10 languages, and the results show that our probabilistic parsing action models outperform the original deterministic dependency parser.