Semantic Dependency Parsing
14 papers with code • 3 benchmarks • 0 datasets
Identify semantic relationships between words in a text using a graph representation.
Most implemented papers
Second-Order Semantic Dependency Parsing with End-to-End Neural Networks
wangxinyu0922/Second_Order_SDP
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ACL 2019
Semantic dependency parsing aims to identify semantic relationships between words in a sentence that form a graph.
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.
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.
Automated Concatenation of Embeddings for Structured Prediction
Pretrained contextualized embeddings are powerful word representations for structured prediction tasks.
Deep Multitask Learning for Semantic Dependency Parsing
We present a deep neural architecture that parses sentences into three semantic dependency graph formalisms.
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.
Multi-Task Semantic Dependency Parsing with Policy Gradient for Learning Easy-First Strategies
shuheikurita/semrl
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ACL 2019
In Semantic Dependency Parsing (SDP), semantic relations form directed acyclic graphs, rather than trees.
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.
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.
N-LTP: An Open-source Neural Language Technology Platform for Chinese
HIT-SCIR/ltp
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EMNLP (ACL) 2021
We introduce \texttt{N-LTP}, an open-source neural language technology platform supporting six fundamental Chinese NLP tasks: {lexical analysis} (Chinese word segmentation, part-of-speech tagging, and named entity recognition), {syntactic parsing} (dependency parsing), and {semantic parsing} (semantic dependency parsing and semantic role labeling).
