Transition-Based Dependency Parsing

11 papers with code • 0 benchmarks • 0 datasets

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Most implemented papers

Transition-Based Dependency Parsing with Stack Long Short-Term Memory

clab/lstm-parser IJCNLP 2015

We propose a technique for learning representations of parser states in transition-based dependency parsers.

Efficient Structured Inference for Transition-Based Parsing with Neural Networks and Error States

sagae/nndep TACL 2016

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.

A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing

datquocnguyen/jPTDP CONLL 2017

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

tzshi/dp-parser-emnlp17 EMNLP 2017

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.

Distilling Knowledge for Search-based Structured Prediction

Oneplus/twpipe ACL 2018

Many natural language processing tasks can be modeled into structured prediction and solved as a search problem.

Joint Learning of POS and Dependencies for Multilingual Universal Dependency Parsing

bcmi220/joint_stackptr CONLL 2018

This paper describes the system of team LeisureX in the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies.

Tree-Stack LSTM in Transition Based Dependency Parsing

kirnap/ku-dependency-parser2 CONLL 2018

We introduce tree-stack LSTM to model state of a transition based parser with recurrent neural networks.

Bidirectional Transition-Based Dependency Parsing

yuanyunzhe/bi-trans-parser AAAI 2019

Traditionally, a transitionbased dependency parser processes an input sentence and predicts a sequence of parsing actions in a left-to-right manner.