OLAC Record oai:www.ldc.upenn.edu:LDC2020T02

Metadata
Title:		Abstract Meaning Representation (AMR) Annotation Release 3.0
Access Rights:		Licensing Instructions for Subscription & Standard Members, and Non-Members: http://www.ldc.upenn.edu/language-resources/data/obtaining
Bibliographic Citation:		Knight, Kevin , et al. Abstract Meaning Representation (AMR) Annotation Release 3.0 LDC2020T02. Web Download. Philadelphia: Linguistic Data Consortium, 2020
Contributor:		Knight, Kevin
		Badarau, Bianca
		Baranescu, Laura
		Bonial, Claire
		Bardocz, Madalina
		Griffitt, Kira
		Hermjakob, Ulf
		Marcu, Daniel
		Palmer, Martha
		O'Gorman, Tim
		Schneider, Nathan
Date (W3CDTF):		2020
Date Issued (W3CDTF):		2020-01-15
Description:		*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.
Extent:		Corpus size: 153936 KB
Identifier:		LDC2020T02
		https://catalog.ldc.upenn.edu/LDC2020T02
		ISBN: 1-58563-915-X
		ISLRN: 676-697-177-821-8
		DOI: 10.35111/44cy-bp51
Language:		English
Language (ISO639):		eng
License:		LDC User Agreement for Non-Members: https://catalog.ldc.upenn.edu/license/ldc-non-members-agreement.pdf
Medium:		Distribution: Web Download
Publisher:		Linguistic Data Consortium
Publisher (URI):		https://www.ldc.upenn.edu
Relation (URI):		https://catalog.ldc.upenn.edu/docs/LDC2020T02
Rights Holder:		Portions © 1994-1996, 2002-2010 Agence France Presse, © 2007 Al-Ahram, © 2007 Al Hayat, © 2007 Al-Quds Al-Arabi, © 2000 American Broadcasting Company, © 2007 An Nahar, © 2007 Asharq Al-Awsat, © 2007 Assabah, © 2002-2008, 2010 The Associated Press, © 2000 Cable News Network LP, LLLP, © 2003-2004, 2007-2008 Central News Agency (Taiwan), © 1997, 2004-2007 China Central TV, © 2007 China Military Online, © 2007 Chinanews.com, © 1987-1989 Dow Jones & Company, Inc., © 2007 Guangming Daily, © 1995, 2003, 2005, 2007-2008 Los Angeles Times-Washington Post News Service, Inc., © 2000 National Broadcasting Company, Inc., © 1999, 2002, 2004-2008, 2010 New York Times, © 2000 Public Radio International, © 1994-1998, 2001-2008 Xinhua News Agency, © 2020 Trustees of the University of Pennsylvania
Type (DCMI):		Text
Type (OLAC):		primary_text
OLAC Info
Archive:		The LDC Corpus Catalog
Description:		http://www.language-archives.org/archive/www.ldc.upenn.edu
GetRecord:		OAI-PMH request for OLAC format
GetRecord:		Pre-generated XML file
OAI Info
OaiIdentifier:		oai:www.ldc.upenn.edu:LDC2020T02
DateStamp:		2021-06-28
GetRecord:		OAI-PMH request for simple DC format
Search Info
Citation:		Knight, Kevin; Badarau, Bianca; Baranescu, Laura; Bonial, Claire; Bardocz, Madalina; Griffitt, Kira; Hermjakob, Ulf; Marcu, Daniel; Palmer, Martha; O'Gorman, Tim; Schneider, Nathan. 2020. Linguistic Data Consortium.
Terms:		area_Europe country_GB dcmi_Text iso639_eng olac_primary_text