Dependency Parsing
323 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
Latest papers with no code
Linear-Time Modeling of Linguistic Structure: An Order-Theoretic Perspective
We show that these exhaustive comparisons can be avoided, and, moreover, the complexity of such tasks can be reduced to linear by casting the relation between tokens as a partial order over the string.
Structured Sentiment Analysis as Transition-based Dependency Parsing
Structured sentiment analysis (SSA) aims to automatically extract people's opinions from a text in natural language and adequately represent that information in a graph structure.
DORIC : Domain Robust Fine-Tuning for Open Intent Clustering through Dependency Parsing
We present our work on Track 2 in the Dialog System Technology Challenges 11 (DSTC11).
Dual-Attention Model for Aspect-Level Sentiment Classification
I propose a novel dual-attention model(DAM) for aspect-level sentiment classification.
Focusing On Targets For Improving Weakly Supervised Visual Grounding
Weakly supervised visual grounding aims to predict the region in an image that corresponds to a specific linguistic query, where the mapping between the target object and query is unknown in the training stage.
Syntactic Structure Processing in the Brain while Listening
In this study, we investigate the predictive power of the brain encoding models in three settings: (i) individual performance of the constituency and dependency syntactic parsing based embedding methods, (ii) efficacy of these syntactic parsing based embedding methods when controlling for basic syntactic signals, (iii) relative effectiveness of each of the syntactic embedding methods when controlling for the other.
Zero-shot cross-lingual transfer language selection using linguistic similarity
This allows us to select a more suitable transfer language which can be used to better leverage knowledge from high-resource languages in order to improve the performance of language applications lacking data.
Weakly Supervised Headline Dependency Parsing
English news headlines form a register with unique syntactic properties that have been documented in linguistics literature since the 1930s.
SGRAM: Improving Scene Graph Parsing via Abstract Meaning Representation
To this end, we design a simple yet effective two-stage scene graph parsing framework utilizing abstract meaning representation, SGRAM (Scene GRaph parsing via Abstract Meaning representation): 1) transforming a textual description of an image into an AMR graph (Text-to-AMR) and 2) encoding the AMR graph into a Transformer-based language model to generate a scene graph (AMR-to-SG).
ATP: A holistic attention integrated approach to enhance ABSA
In the case of ABSA, aspect position plays a vital role.
