Abstract
Introduction
Abstract Meaning Representation (AMR) Annotation Release 3.0 was developed by the Linguistic Data Consortium (LDC), SDL/Language Weaver, Inc. , the University of Colorado's Computational Language and Educational Research group and the Information Sciences Institute at the University of Southern California. It contains a sembank (semantic treebank) of over 59,255 English natural language sentences from broadcast conversations, newswire, weblogs, web discussion forums, fiction and web text. This release adds new data to, and updates material contained in, Abstract Meaning Representation 2.0 ( LDC2017T10 ), specifically: more annotations on new and prior data, new or improved PropBank-style frames, enhanced quality control, and multi-sentence annotations.
AMR captures "who is doing what to whom" in a sentence. Each sentence is paired with a graph that represents its whole-sentence meaning in a tree-structure. AMR utilizes PropBank frames, non-core semantic roles, within-sentence coreference, named entity annotation, modality, negation, questions, quantities, and so on to represent the semantic structure of a sentence largely independent of its syntax.
LDC also released Abstract Meaning Representation (AMR) Annotation Release 1.0 ( LDC2014T12 ), and Abstract Meaning Representation (AMR) Annotation Release 2.0 ( LDC2017T10 ).
Data
The source data includes discussion forums collected for the DARPA BOLT AND DEFT programs, transcripts and English translations of Mandarin Chinese broadcast news programming from China Central TV, Wall Street Journal text, translated Xinhua news texts, various newswire data from NIST OpenMT evaluations and weblog data used in the DARPA GALE program. New source data to AMR 3.0 includes sentences from Aesop's Fables, parallel text and the situation frame data set developed by LDC for the DARPA LORELEI program, and lead sentences from Wikipedia articles about named entities.
The following table summarizes the number of training, dev, and test AMRs for each dataset in the release. Totals are also provided by partition and dataset:
Dataset
Training
Dev
Test
Totals
BOLT DF MT
1061
133
133
1327
Broadcast conversation
214
0
0
214
Weblog and WSJ
0
100
100
200
BOLT DF English
7379
210
229
7818
DEFT DF English
32915
0
0
32915
Aesop fables
49
0
0
49
Guidelines AMRs
970
0
0
970
LORELEI
4441
354
527
5322
2009 Open MT
204
0
0
204
Proxy reports
6603
826
823
8252
Weblog
866
0
0
866
Wikipedia
192
0
0
192
Xinhua MT
741
99
86
926
Totals
55635
1722
1898
59255
Data in the "split" directory contains 59,255 AMRs split roughly 93.9%/2.9%/3.2% into training/dev/test partitions, with most smaller datasets assigned to one of the splits as a whole. Note that splits observe document boundaries. The "unsplit" directory contains the same 59,255 AMRs with no train/dev/test partition.
Samples
Please view this AMR sample .
Updates
None at this time.
Acknowledgements
From University of Colorado
We gratefully acknowledge the support of the National Science Foundation Grant NSF: 0910992 IIS:RI: Large: Collaborative Research: Richer Representations for Machine Translation and the support of Darpa BOLT - HR0011-11-C-0145 and DEFT - FA-8750-13-2-0045 via a subcontract from LDC. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation, DARPA or the US government.
From Information Sciences Institute (ISI)
Thanks to NSF (IIS-0908532) for funding the initial design of AMR, and to DARPA MRP (FA-8750-09-C-0179) for supporting a group to construct consensus annotations and the AMR Editor. The initial AMR bank was built under DARPA DEFT FA-8750-13-2-0045 (PI: Stephanie Strassel; co-PIs: Kevin Knight, Daniel Marcu, and Martha Palmer) and DARPA BOLT HR0011-12-C-0014 (PI: Kevin Knight).
From Linguistic Data Consortium (LDC)
This material is based on research sponsored by Air Force Research Laboratory and Defense Advance Research Projects Agency under agreement number FA8750-13-2-0045. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of Air Force Research Laboratory and Defense Advanced Research Projects Agency or the U.S. Government.
We gratefully acknowledge the support of Defense Advanced Research Projects Agency (DARPA) Machine Reading Program under Air Force Research Laboratory (AFRL) prime contract no. FA8750-09-C-0184 Subcontract 4400165821. Any opinions, findings, and conclusion or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the view of the DARPA, AFRL, or the US government.
From Language Weaver (SDL)
This work was partially sponsored by DARPA contract HR0011-11-C-0150 to LanguageWeaver Inc. Any opinions, findings, and conclusion or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the view of the DARPA or the US government.
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