Reorient: Resources for Operationally Relevant Information Extraction from Non-Explicit Text
Abstract
The ReORIENT (Resources for Operationally Relevant Information Extraction from Non-explicit Text) research effort has developed a set of linguistic resources to support deep natural language understanding in the context of a diffuse, diverse and large community of researchers and stakeholders. To support Relational Analysis research we have developed data sets labeled for entities, relations, events, and AMR sembanking. To support Anomaly Analysis research we have developed resources labeled for sentiment and belief-based and event-based phenomena. To support Smart Filtering research we have created data sets labeled for textual entailment and inference. A total of 240 distinct data sets were developed under this effort and distributed to DEFT performers during the program. These resources have been consolidated into 34 corpora that have been or will soon be published in LDCs public catalog, making DEFT data available to the wider research community, thus amplifying the governments investment in linguistic data and stimulating relevant research outside of the program.
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 26, 2019
- Accession Number
- AD1067825
Entities
People
- Ann Bies
- Jennifer Tracey
- Jeremy Getman
- Kevin Knight
- Kira Griffitt
- Martha Palmer
- Stephanie Strassel
- Zhiyi Song
Organizations
- University of Pennsylvania