tailieunhanh - Báo cáo khoa học: "Tree-Based Deterministic Dependency Parsing"

Nivre’s method was improved by enhancing deterministic dependency parsing through application of a tree-based model. The model considers all words necessary for selection of parsing actions by including words in the form of trees. It chooses the most probable head candidate from among the trees and uses this candidate to select a parsing action. In an evaluation experiment using the Penn Treebank (WSJ section), the proposed model achieved higher accuracy than did previous deterministic models. Although the proposed model’s worst-case time complexity is O(n2 ), the experimental results demonstrated an average parsing time not much slower than O(n) | Tree-Based Deterministic Dependency Parsing An Application to Nivre s Method Kotaro Kitagawa Kumiko Tanaka-Ishii Graduate School of Information Science and Technology The University of Tokyo kitagawa@ kumiko@ Abstract Nivre s method was improved by enhancing deterministic dependency parsing through application of a tree-based model. The model considers all words necessary for selection of parsing actions by including words in the form of trees. It chooses the most probable head candidate from among the trees and uses this candidate to select a parsing action. In an evaluation experiment using the Penn Treebank WSJ section the proposed model achieved higher accuracy than did previous deterministic models. Although the proposed model s worst-case time complexity is O n2 the experimental results demonstrated an average parsing time not much slower than O n . 1 Introduction Deterministic parsing methods achieve both effective time complexity and accuracy not far from those of the most accurate methods. One such deterministic method is Nivre s method an incremental parsing method whose time complexity is linear in the number of words Nivre 2003 . Still deterministic methods can be improved. As a specific example Nivre s model greedily decides the parsing action only from two words and their locally relational words which can lead to errors. In the field of Japanese dependency parsing Iwatate et al. 2008 proposed a tournament model that takes all head candidates into account in judging dependency relations. This method assumes backward parsing because the Japanese dependency structure has a head-final constraint so that any word s head is located to its right. Here we propose a tree-based model applicable to any projective language which can be considered as a kind of generalization of Iwatate s idea. Instead of selecting a parsing action for two words as in Nivre s model our tree-based model first chooses the most probable head .