transition-amr-parser and stog

transition-amr-parser
50
Established
stog
47
Emerging
Maintenance 2/25
Adoption 10/25
Maturity 16/25
Community 22/25
Maintenance 0/25
Adoption 10/25
Maturity 16/25
Community 21/25
Stars: 273
Forks: 56
Downloads: —
Commits (30d): 0
Language: Python
License: Apache-2.0
Stars: 156
Forks: 36
Downloads: —
Commits (30d): 0
Language: Python
License: MIT
Stale 6m No Package No Dependents
Stale 6m No Package No Dependents

About transition-amr-parser

IBM/transition-amr-parser

SoTA Abstract Meaning Representation (AMR) parsing with word-node alignments in Pytorch. Includes checkpoints and other tools such as statistical significance Smatch.

This tool helps natural language processing researchers and developers convert plain text into Abstract Meaning Representation (AMR) graphs. You input English or other supported language sentences or documents, and it outputs a graphical representation of their meaning in Penman notation. This is for users who need to analyze semantic structures in text for research or advanced language understanding tasks.

Natural Language Processing Computational Linguistics Semantic Parsing Text Analysis AI/ML Research

About stog

sheng-z/stog

AMR Parsing as Sequence-to-Graph Transduction

This project helps Natural Language Processing (NLP) researchers and computational linguists convert English sentences into Abstract Meaning Representations (AMR). It takes raw text as input and outputs a graph that shows the semantic meaning of the sentence, including who did what to whom. This is useful for researchers who need to analyze sentence meaning in a structured, machine-readable format.

Natural Language Processing Computational Linguistics Semantic Parsing Meaning Representation NLP Research

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