Text Analysis Conference
The Text Analysis Conference (TAC) is a series of evaluation workshops
organized to encourage research in Natural Language Processing and
related applications, by providing a large test collection, common
evaluation procedures, and a forum for organizations to share their
results. TAC comprises sets of tasks known as "tracks," each of which
focuses on a particular subproblem of NLP. TAC tracks focus on
end-user tasks, but also include component evaluations situated within
the context of end-user tasks.
TAC 2020 hosts evaluations in three tracks:
Epidemic Question Answering (EPIC-QA)
The goal of the EPIC-QA track is to evaluate systems on their
ability to provide timely and well-supported answers to questions
about the disease COVID-19, its causal virus SARS-CoV-2, related
coronaviruses, and the recommended response to the
pandemic. Because questions arise from both experts and
non-experts in the field, EPIC-QA systems are challenged to return
expert-level answers as expected by the scientific and medical
communities as well as answers in consumer-friendly language for
the general public.
- Recognizing Ultra Fine-Grained Entities (RUFES)
The goal of the KBP RUFES track is to extract and corefer mentions of fine-grained entity types in text.
- Streaming Multimedia Knowledge Base Population (SM-KBP)
The goal of the SM-KBP track is to develop and evaluate technologies that extract structured Knowledge Elements (KEs) from a variety of unstructured sources in order to generate explicit alternative interpretations of events, situations, and trends in noisy, conflicting, and potentially deceptive information environments.
NIST is an agency of the
U.S. Department of Commerce
Last updated: Wednesday, 16-Sep-2020 10:03:32 MDT
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