Machine Reading Phase 1 NFL Scoring Training Data
|Item Name:||Machine Reading Phase 1 NFL Scoring Training Data|
|Author(s):||Heather Simpson, Stephanie Strassel, Jonathan Wright, Kira Griffitt|
|LDC Catalog No.:||LDC2019T14|
|Release Date:||September 16, 2019|
|Application(s):||machine reading, knowledge representation, information extraction|
LDC User Agreement for Non-Members
|Online Documentation:||LDC2019T14 Documents|
|Licensing Instructions:||Subscription & Standard Members, and Non-Members|
|Citation:||Simpson, Heather, et al. Machine Reading Phase 1 NFL Scoring Training Data LDC2019T14. Web Download. Philadelphia: Linguistic Data Consortium, 2019.|
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Machine Reading Phase 1 NFL Scoring Training Data was developed by the Linguistic Data Consortium (LDC) and contains 110 US NFL (National Football League) scoring source documents and 110 standoff annotation files used in the DARPA (Defense Advanced Research Projects Agency) Machine Reading program.
The Machine Reading program aimed to develop automated reading systems to bridge the gap between knowledge contained in natural language texts and knowledge accessible to formal reasoning systems. The reading systems designed by program participants were required to extract and reason about facts from text in multiple domains.
The data in this release constitutes the training data for the NFL Scoring Use Cases evaluation. The NFL Scoring Use Cases tested the sports domain by extracting information about scoring events and outcomes of US NFL games and by aligning that information with an NFL Scoring ontology.
This release contains 110 source documents (70,233 words) from English newswire stories. The files were manually annotated for instances of NFL Scoring annotation categories defined with respect to the NFL Scoring ontology.
Annotations are in GUI XML (traditional annotation) and RDF XML (formal knowledge representation) formats. All source and annotation files are presented as UTF-8 encoded XML files with associated dtds.
The Linguistic Data Consortium gratefully acknowledges the support of Defense Advanced Research Projects Agency (DARPA) Machine Reading Program under Air Force Research Laboratory (AFRL) prime contract no. FA8750-09 C-xxxx. 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.
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