Abstract Meaning Representation (AMR) Annotation Release 1.0

Item Name: Abstract Meaning Representation (AMR) Annotation Release 1.0
Author(s): Kevin Knight, Laura Baranescu, Claire Bonial, Madalina Georgescu, Kira Griffitt, Ulf Hermjakob, Daniel Marcu, Martha Palmer, Nathan Schneider
LDC Catalog No.: LDC2014T12
ISBN: 1-58563-681-9
ISLRN: 637-196-362-554-6
Release Date: June 16, 2014
Member Year(s): 2014
DCMI Type(s): Text
Data Source(s): discussion forum, weblogs, newswire, web collection
Project(s): BOLT, DEFT
Application(s): information extraction, entity extraction, semantic role labelling, coreference resolution
Language(s): English
Language ID(s): eng
License(s): LDC User Agreement for Non-Members
Online Documentation: LDC2014T12 Documents
Licensing Instructions: Subscription & Standard Members, and Non-Members
Citation: Knight, Kevin, et al. Abstract Meaning Representation (AMR) Annotation Release 1.0 LDC2014T12. Web Download. Philadelphia: Linguistic Data Consortium, 2014.

Introduction

Abstract Meaning Representation (AMR) Annotation Release 1.0, Linguistic Data Consortium (LDC) Catalog Number LDC2014T12 and ISBN 1-58563-677-0, was developed by 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 13,000 English natural language sentences from newswire, weblogs and web discussion forums.

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.

Data

The source data includes discussion forums collected for the DARPA BOLT program, Wall Street Journal and translated Xinhua news texts, various newswire data from NIST OpenMT evaluations and weblog data used in the DARPA GALE program. 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
Weblog and WSJ 0 100 100 200
BOLT DF English 1703 210 229 2142
2009 Open MT 204 0 0 204
Xinhua MT 741 99 86 926
Totals 3709 542 548 4799

 

For those interested in a utilizing a standard/community partition for AMR research (for instance in development of semantic parsers), data in the "split" directory contains 13,051 AMRs divided roughly 80/10/10 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 13,051 AMRs with no train/dev/test partition.

Samples

Please view this 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

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

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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