Head-driven Transition-based Parsing with Top-down Prediction

Katsuhiko Hayashi, Taro Watanabe, Masayuki Asahara, Yuji Matsumoto

Abstract: This paper presents a novel top-down head-driven parsing algorithm for data-driven projective dependency analysis. This algorithm handles global structures, such as clause and coordination, better than shift-reduce or other bottom-up algorithms. Experiments on the English Penn Treebank data and the Chinese CoNLL-06 data show that the proposed algorithm achieves comparable results with other data-driven dependency parsing algorithms.