Dependency Parsing
322 papers with code • 15 benchmarks • 14 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
CoNLL
GENIA
KLUE
GUM
Subtasks
Most implemented papers
Seq2seq Dependency Parsing
bcmi220/seq2seq_parser
•
•
COLING 2018
This paper presents a sequence to sequence (seq2seq) dependency parser by directly predicting the relative position of head for each given word, which therefore results in a truly end-to-end seq2seq dependency parser for the first time.
LINSPECTOR: Multilingual Probing Tasks for Word Representations
We present a reusable methodology for creation and evaluation of such tests in a multilingual setting.
75 Languages, 1 Model: Parsing Universal Dependencies Universally
hyperparticle/udify
•
•
IJCNLP 2019
We present UDify, a multilingual multi-task model capable of accurately predicting universal part-of-speech, morphological features, lemmas, and dependency trees simultaneously for all 124 Universal Dependencies treebanks across 75 languages.
Generalizing Natural Language Analysis through Span-relation Representations
Natural language processing covers a wide variety of tasks predicting syntax, semantics, and information content, and usually each type of output is generated with specially designed architectures.
Parsing as Pretraining
huggingface/pytorch-pretrained-BERT
•
•
We first cast constituent and dependency parsing as sequence tagging.
KLUE: Korean Language Understanding Evaluation
We introduce Korean Language Understanding Evaluation (KLUE) benchmark.
Yara Parser: A Fast and Accurate Dependency Parser
At its fastest, Yara can parse about 4000 sentences per second when in greedy mode (1 beam).
Neural End-to-End Learning for Computational Argumentation Mining
Contrary to models that operate on the argument component level, we find that framing AM as dependency parsing leads to subpar performance results.
VnCoreNLP: A Vietnamese Natural Language Processing Toolkit
We present an easy-to-use and fast toolkit, namely VnCoreNLP---a Java NLP annotation pipeline for Vietnamese.
Scene Graph Parsing as Dependency Parsing
Yusics/bist-parser
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•
NAACL 2018
The scene graphs generated by our learned neural dependency parser achieve an F-score similarity of 49. 67% to ground truth graphs on our evaluation set, surpassing best previous approaches by 5%.
