An Incremental Parser for Abstract Meaning Representation
TL
TL;DR
A transition-based parser for AMR that parses sentences left-to-right, in linear time is described and it is shown that this parser is competitive with the state of the art on the LDC2015E86 dataset and that it outperforms state-of-the-art parsers for recovering named entities and handling polarity.
Abstract
Abstract Meaning Representation (AMR) is a semantic representation for natural language that embeds annotations related to traditional tasks such as named entity recognition, semantic role labeling, word sense disambiguation and co-reference resolution. We describe a transition-based parser for AMR that parses sentences left-to-right, in linear time. We further propose a test-suite that assesses specific subtasks that are helpful in comparing AMR parsers, and show that our parser is competitive with the state of the art on the LDC2015E86 dataset and that it outperforms state-of-the-art parsers for recovering named entities and handling polarity.
References38 items
1
AMR Parsing with an Incremental Joint Model
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Squib: Expressive Power of Abstract Meaning Representations
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Noise reduction and targeted exploration in imitation learning for Abstract Meaning Representation parsing
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CMU at SemEval-2016 Task 8: Graph-based AMR Parsing with Infinite Ramp Loss
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