Dependency Parsing
315 papers with code • 15 benchmarks • 13 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
These leaderboards are used to track progress in Dependency Parsing
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Libraries
Use these libraries to find Dependency Parsing models and implementationsDatasets
Penn Treebank
Universal Dependencies
GENIA
English Web Treebank
KLUE
GUM
Subtasks
Most implemented papers
Deep Biaffine Attention for Neural Dependency Parsing
This paper builds off recent work from Kiperwasser & Goldberg (2016) using neural attention in a simple graph-based dependency parser.
Transition-Based Dependency Parsing with Stack Long Short-Term Memory
We propose a technique for learning representations of parser states in transition-based dependency parsers.
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.
SciBERT: A Pretrained Language Model for Scientific Text
allenai/scibert
•
•
IJCNLP 2019
Obtaining large-scale annotated data for NLP tasks in the scientific domain is challenging and expensive.
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.
Second-Order Semantic Dependency Parsing with End-to-End Neural Networks
wangxinyu0922/Second_Order_SDP
•
•
ACL 2019
Semantic dependency parsing aims to identify semantic relationships between words in a sentence that form a graph.
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.
SparseMAP: Differentiable Sparse Structured Inference
Structured prediction requires searching over a combinatorial number of structures.
Stack-Pointer Networks for Dependency Parsing
XuezheMax/NeuroNLP2
•
•
ACL 2018
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.
Simpler but More Accurate Semantic Dependency Parsing
While syntactic dependency annotations concentrate on the surface or functional structure of a sentence, semantic dependency annotations aim to capture between-word relationships that are more closely related to the meaning of a sentence, using graph-structured representations.
