Dependency Parsing
322 papers with code • 14 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
Learning Joint Semantic Parsers from Disjoint Data
We present a new approach to learning semantic parsers from multiple datasets, even when the target semantic formalisms are drastically different, and the underlying corpora do not overlap.
Building a Kannada POS Tagger Using Machine Learning and Neural Network Models
Pruthwik/Training-POS-Chunk-Tagger-Using-BILSTM
•
•
POS Tagging serves as a preliminary task for many NLP applications.
From POS tagging to dependency parsing for biomedical event extraction
Results: We perform an empirical study comparing state-of-the-art traditional feature-based and neural network-based models for two core natural language processing tasks of part-of-speech (POS) tagging and dependency parsing on two benchmark biomedical corpora, GENIA and CRAFT.
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.
On Difficulties of Cross-Lingual Transfer with Order Differences: A Case Study on Dependency Parsing
uclanlp/CrossLingualDepParser
•
•
NAACL 2019
Different languages might have different word orders.
Multi-source synthetic treebank creation for improved cross-lingual dependency parsing
This paper describes a method of creating synthetic treebanks for cross-lingual dependency parsing using a combination of machine translation (including pivot translation), annotation projection and the spanning tree algorithm.
Glyce: Glyph-vectors for Chinese Character Representations
ShannonAI/glyce
•
•
NeurIPS 2019
However, due to the lack of rich pictographic evidence in glyphs and the weak generalization ability of standard computer vision models on character data, an effective way to utilize the glyph information remains to be found.
Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing
TalSchuster/CrossLingualELMo
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NAACL 2019
We introduce a novel method for multilingual transfer that utilizes deep contextual embeddings, pretrained in an unsupervised fashion.
Left-to-Right Dependency Parsing with Pointer Networks
danifg/Left2Right-Pointer-Parser
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•
We propose a novel transition-based algorithm that straightforwardly parses sentences from left to right by building $n$ attachments, with $n$ being the length of the input sentence.
Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT
shijie-wu/crosslingual-nlp
•
•
IJCNLP 2019
Pretrained contextual representation models (Peters et al., 2018; Devlin et al., 2018) have pushed forward the state-of-the-art on many NLP tasks.
