A Top-Down Model for Character-Level Chinese Dependency Parsing

Abstract

This paper proposes a novel transition-based algorithm for character-level Chinese dependency parsing that straightforwardly models the dependency tree in a top-down manner. Based on the stack-pointer parser, we joint Chinese word segmentation, part-of-speech tagging, and dependency parsing in a new way. We recursively build the character-based dependency tree from root to leaf in a depth-first fashion, by searching for candidate dependents through the sentence and predicting relation type at each step. We introduce intra-word dependencies into the relation types for word segmentation, and the inter-word dependencies with POS tags for part-of-speech tagging. Since the top-down model provides a global view of an input sentences, the information of the whole sentence and all previously generated arcs are available for action decisions, and all characters of the sentence are considered as candidate dependencies. Experimental results on the Penn Chinese Treebank (CTB) show that the proposed model outperformed existing neural joint parsers by 0.81% on dependency parsing, and achieved the F1-scores of 95.97%, 91.72%, 80.25% for Chinese word segmentation, part-of-speech tagging, and dependency parsing.