Dependency Parsing
Dependency parsing is the task of extracting a dependency parse of a sentence that represents its grammatical structure and defines the relationships between "head" words and words, which modify those heads.
Example:
root
|
| +-------dobj---------+
| | |
nsubj | | +------det-----+ | +-----nmod------+
+--+ | | | | | | |
| | | | | +-nmod-+| | | +-case-+ |
+ | + | + + || + | + | |
I prefer the morning flight through Denver
Relations among the words are illustrated above the sentence with directed, labeled arcs from heads to dependents (+ indicates the dependent).
State-of-the-art leaderboards
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Subtasks
Greatest papers with code
DRAGNN: A Transition-based Framework for Dynamically Connected Neural Networks
In this work, we present a compact, modular framework for constructing novel recurrent neural architectures.
Globally Normalized Transition-Based Neural Networks
ACL 2016
• tensorflow/models
• 
Our model is a simple feed-forward neural network that operates on a task-specific transition system, yet achieves comparable or better accuracies than recurrent models.
#3 best model for
Dependency Parsing
on Penn Treebank
DEPENDENCY PARSING PART-OF-SPEECH TAGGING SENTENCE COMPRESSION
Semi-Supervised Sequence Modeling with Cross-View Training
EMNLP 2018
• tensorflow/models
•
We therefore propose Cross-View Training (CVT), a semi-supervised learning algorithm that improves the representations of a Bi-LSTM sentence encoder using a mix of labeled and unlabeled data.
SOTA for CCG Supertagging on CCGBank
CCG SUPERTAGGING DEPENDENCY PARSING MACHINE TRANSLATION MULTI-TASK LEARNING NAMED ENTITY RECOGNITION (NER) UNSUPERVISED REPRESENTATION LEARNING
Deep Biaffine Attention for Neural Dependency Parsing
• dmlc/gluon-nlp
• 
This paper builds off recent work from Kiperwasser & Goldberg (2016) using neural attention in a simple graph-based dependency parser.
#2 best model for
Dependency Parsing
on Penn Treebank
Stack-Pointer Networks for Dependency Parsing
ACL 2018
• XuezheMax/NeuroNLP2
• 
Combining pointer networks~\citep{vinyals2015pointer} with an internal stack, the proposed model first reads and encodes the whole sentence, then builds the dependency tree top-down (from root-to-leaf) in a depth-first fashion.
Transition-Based Dependency Parsing with Stack Long Short-Term Memory
IJCNLP 2015
• elikip/bist-parser
•
We propose a technique for learning representations of parser states in transition-based dependency parsers.
Improved Transition-Based Parsing by Modeling Characters instead of Words with LSTMs
We present extensions to a continuous-state dependency parsing method that makes it applicable to morphologically rich languages.