Dependency Parsing

Edit

114 papers with code · Natural Language Processing

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).

Leaderboards

Add a Result

DATASET	BEST METHOD	PAPER TITLE	COMPARE
Penn Treebank	CVT + Multi-Task	Semi-Supervised Sequence Modeling with Cross-View Training	See all
GENIA - LAS	BiLSTM-CRF	From POS tagging to dependency parsing for biomedical event extraction	See all
GENIA - UAS	BiLSTM-CRF	From POS tagging to dependency parsing for biomedical event extraction	See all

Subtasks

Transition-Based Dependency Parsing

7 papers with code

Dependency Grammar Induction

1 leaderboard

2 papers with code

Prepositional Phrase Attachment

2 papers with code

Greatest papers with code

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 UNSUPERVISED REPRESENTATION LEARNING

62,866

Paper
Code

DRAGNN: A Transition-based Framework for Dynamically Connected Neural Networks

13 Mar 2017 • tensorflow/models •

In this work, we present a compact, modular framework for constructing novel recurrent neural architectures.

DEPENDENCY PARSING MULTI-TASK LEARNING

61,770

Paper
Code

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.

#4 best model for Dependency Parsing on Penn Treebank

DEPENDENCY PARSING PART-OF-SPEECH TAGGING SENTENCE COMPRESSION

61,770

Paper
Code

CamemBERT: a Tasty French Language Model

10 Nov 2019 • huggingface/transformers •

We measure the performance of CamemBERT compared to multilingual models in multiple downstream tasks, namely part-of-speech tagging, dependency parsing, named-entity recognition, and natural language inference.

DEPENDENCY PARSING LANGUAGE MODELLING NAMED ENTITY RECOGNITION NATURAL LANGUAGE INFERENCE PART-OF-SPEECH TAGGING

24,702

Paper
Code