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
DisSent: Sentence Representation Learning from Explicit Discourse Relations
• facebookresearch/InferSent
• 
Learning effective representations of sentences is one of the core missions of natural language understanding.
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.
SciBERT: Pretrained Contextualized Embeddings for Scientific Text
• allenai/scibert
•
Obtaining large-scale annotated data for NLP tasks in the scientific domain is challenging and expensive.
SOTA for Named Entity Recognition (NER) on BC5CDR (using extra training data)
CITATION INTENT CLASSIFICATION DEPENDENCY PARSING MEDICAL NAMED ENTITY RECOGNITION PARTICIPANT INTERVENTION COMPARISON OUTCOME EXTRACTION RELATION EXTRACTION SENTENCE CLASSIFICATION