AMR Beyond the Sentence: the Multi-sentence AMR corpus

Tim O’Gorman, Michael Regan, Kira Griffitt, Ulf Hermjakob, Kevin Knight, Martha Palmer

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Abstract
There are few corpora that endeavor to represent the semantic content of entire documents. We present a corpus that accomplishes one way of capturing document level semantics, by annotating coreference and similar phenomena (bridging and implicit roles) on top of gold Abstract Meaning Representations of sentence-level semantics. We present a new corpus of this annotation, with analysis of its quality, alongside a plausible baseline for comparison. It is hoped that this Multi-Sentence AMR corpus (MS-AMR) may become a feasible method for developing rich representations of document meaning, useful for tasks such as information extraction and question answering.
Anthology ID:
C18-1313
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Editors:
Emily M. Bender, Leon Derczynski, Pierre Isabelle
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3693–3702
Language:
URL:
https://aclanthology.org/C18-1313
DOI:
Bibkey:
Cite (ACL):
Tim O’Gorman, Michael Regan, Kira Griffitt, Ulf Hermjakob, Kevin Knight, and Martha Palmer. 2018. AMR Beyond the Sentence: the Multi-sentence AMR corpus. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3693–3702, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
AMR Beyond the Sentence: the Multi-sentence AMR corpus (O’Gorman et al., COLING 2018)
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PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/C18-1313.pdf