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Graph-based methods are popular in dependency parsing for decades. Recently, citet{yang2021headed} propose a headed span-based method. Both of them score all possible trees and globally find the highest-scoring tree. In this paper, we combine these two kinds of methods, designing several dynamic programming algorithms for joint inference. Experiments show the effectiveness of our proposed methodsfootnote{Our code is publicly available at url{https://github.com/sustcsonglin/span-based-dependency-parsing}.}.
We propose a headed span-based method for projective dependency parsing. In a projective tree, the subtree rooted at each word occurs in a contiguous sequence (i.e., span) in the surface order, we call the span-headword pair textit{headed span}. In t
Most of the unsupervised dependency parsers are based on first-order probabilistic generative models that only consider local parent-child information. Inspired by second-order supervised dependency parsing, we proposed a second-order extension of un
In this paper, we present an approach to improve the accuracy of a strong transition-based dependency parser by exploiting dependency language models that are extracted from a large parsed corpus. We integrated a small number of features based on the
In this paper, we study the problem of parsing structured knowledge graphs from textual descriptions. In particular, we consider the scene graph representation that considers objects together with their attributes and relations: this representation h
Chinese word segmentation and dependency parsing are two fundamental tasks for Chinese natural language processing. The dependency parsing is defined on word-level. Therefore word segmentation is the precondition of dependency parsing, which makes de