An Incremental Parser for Abstract Meaning Representation

TL;DR

This paper introduces a transition-based, linear-time parser for Abstract Meaning Representation that effectively handles named entities and polarity, with a new test suite for subtask evaluation.

Contribution

It presents a novel incremental parser for AMR that is competitive with state-of-the-art methods and includes a test suite for detailed subtask assessment.

Findings

Parser is competitive with state-of-the-art on LDC2015E86

Outperforms others in named entity recovery

Handles polarity more effectively

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.