Dependency Parsing
214 papers with code • 12 benchmarks • 7 datasets
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).
Benchmarks
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Datasets
Penn Treebank
Universal Dependencies
GENIA
English Web Treebank
KLUE
Subtasks
Greatest papers with code
Semi-Supervised Sequence Modeling with Cross-View Training
tensorflow/models
•
•
EMNLP 2018
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.
Ranked #1 on
CCG Supertagging
on CCGbank
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
tensorflow/models
•
•
ACL 2016
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.
Ranked #15 on
Dependency Parsing
on Penn Treebank
CamemBERT: a Tasty French Language Model
huggingface/transformers
•
•
ACL 2020
We show that the use of web crawled data is preferable to the use of Wikipedia data.
Ranked #1 on
Dependency Parsing
on Spoken Corpus
Stanza: A Python Natural Language Processing Toolkit for Many Human Languages
stanfordnlp/stanza
•
•
ACL 2020
We introduce Stanza, an open-source Python natural language processing toolkit supporting 66 human languages.
N-LTP: A Open-source Neural Chinese Language Technology Platform with Pretrained Models
HIT-SCIR/ltp
•
•
In addition, knowledge distillation where the single-task model teaches the multi-task model is further introduced to encourage the multi-task model to surpass its single-task teacher.
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.
Ranked #2 on
Dependency Parsing
on CoNLL-2009
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.
Towards Better UD Parsing: Deep Contextualized Word Embeddings, Ensemble, and Treebank Concatenation
HIT-SCIR/ELMoForManyLangs
•
•
CONLL 2018
This paper describes our system (HIT-SCIR) submitted to the CoNLL 2018 shared task on Multilingual Parsing from Raw Text to Universal Dependencies.
Ranked #3 on
Dependency Parsing
on Universal Dependencies
