AMR-gs: AMR Parsing via Graph-Sequence Iterative Inference
What it is
A framework for Abstract Meaning Representation (AMR) parsing that utilizes graph-sequence iterative inference. It incorporates graph recategorization to improve the accuracy of semantic representation extraction.
The problem it solves
Standard sequence-to-sequence models often struggle with the complex structural dependencies required for accurate AMR parsing.
What you could use it for
- Parsing complex sentences into Abstract Meaning Representation (AMR)
- Extracting semantic structures from natural language text
- Training and evaluating AMR parsers using pre-trained models
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Summary written by an AI model from the source; check the source before you rely on it.