Semantic Dependency Parsing Edit Task
Natural Language Processing • Semantic Parsing
About
Identify semantic relationships between words in a text using a graph representation.
Benchmarks
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Greatest papers with code
N-LTP: A Open-source Neural Chinese Language Technology Platform with Pretrained Models
• HIT-SCIR/ltp
• 
We introduce N-LTP, an open-source Python Chinese natural language processing toolkit supporting five basic tasks: Chinese word segmentation, part-of-speech tagging, named entity recognition, dependency parsing, and semantic dependency parsing.
CHINESE WORD SEGMENTATION DEPENDENCY PARSING KNOWLEDGE DISTILLATION NAMED ENTITY RECOGNITION PART-OF-SPEECH TAGGING SEMANTIC DEPENDENCY PARSING
Second-Order Semantic Dependency Parsing with End-to-End Neural Networks
Semantic dependency parsing aims to identify semantic relationships between words in a sentence that form a graph.
Ranked #3 on
Semantic Dependency Parsing
on DM
DEPENDENCY PARSING SEMANTIC DEPENDENCY PARSING VARIATIONAL INFERENCE
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.
Ranked #4 on
Semantic Dependency Parsing
on DM
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.
Deep Multitask Learning for Semantic Dependency Parsing
We present a deep neural architecture that parses sentences into three semantic dependency graph formalisms.
Automated Concatenation of Embeddings for Structured Prediction
• Alibaba-NLP/ACE
•
Pretrained contextualized embeddings are powerful word representations for structured prediction tasks.
ASPECT EXTRACTION CHUNKING DEPENDENCY PARSING NAMED ENTITY RECOGNITION NEURAL ARCHITECTURE SEARCH PART-OF-SPEECH TAGGING SEMANTIC DEPENDENCY PARSING STRUCTURED PREDICTION
Semi-Supervised Semantic Dependency Parsing Using CRF Autoencoders
Semantic dependency parsing, which aims to find rich bi-lexical relationships, allows words to have multiple dependency heads, resulting in graph-structured representations.
Backpropagating through Structured Argmax using a SPIGOT
We introduce the structured projection of intermediate gradients optimization technique (SPIGOT), a new method for backpropagating through neural networks that include hard-decision structured predictions (e. g., parsing) in intermediate layers.
DEPENDENCY PARSING SEMANTIC DEPENDENCY PARSING SENTIMENT ANALYSIS
Transition-based Semantic Dependency Parsing with Pointer Networks
Transition-based parsers implemented with Pointer Networks have become the new state of the art in dependency parsing, excelling in producing labelled syntactic trees and outperforming graph-based models in this task.
DEPENDENCY PARSING SEMANTIC DEPENDENCY PARSING WORD EMBEDDINGS
Multi-Task Semantic Dependency Parsing with Policy Gradient for Learning Easy-First Strategies
ACL 2019
• shuheikurita/semrl
• 
In Semantic Dependency Parsing (SDP), semantic relations form directed acyclic graphs, rather than trees.
DEPENDENCY PARSING MULTI-TASK LEARNING SEMANTIC DEPENDENCY PARSING