Transition-Based Dependency Parsing
12 papers with code • 0 benchmarks • 0 datasets
These leaderboards are used to track progress in Transition-Based Dependency Parsing
Transition-based approaches based on local classification are attractive for dependency parsing due to their simplicity and speed, despite producing results slightly below the state-of-the-art.
We present a novel neural network model that learns POS tagging and graph-based dependency parsing jointly.
Fast(er) Exact Decoding and Global Training for Transition-Based Dependency Parsing via a Minimal Feature Set
We first present a minimal feature set for transition-based dependency parsing, continuing a recent trend started by Kiperwasser and Goldberg (2016a) and Cross and Huang (2016a) of using bi-directional LSTM features.
This paper describes the system of team LeisureX in the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies.