Identify semantic relationships between words in a text using a graph representation.
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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.
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
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
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.
We present a deep neural architecture that parses sentences into three semantic dependency graph formalisms.
Pretrained contextualized embeddings are powerful word representations for structured prediction tasks.
Semantic dependency parsing, which aims to find rich bi-lexical relationships, allows words to have multiple dependency heads, resulting in graph-structured representations.
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.
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.