Recognizing Ultra Fine-grained Entities, Events, and Relations (RUFEERS) 2024

Conducted by:
U.S. National Institute of Standards and Technology (NIST)

With support from:
U.S. Department of Defense


The goal of the RUFEERS track is to extract information about entities, events, and relations such that the information would be suitable as input to a knowledge base. Many real world applications in scenarios such as disaster relief and technical support require systems that recognize a wide variety of entity, event, and relation types (e.g., different types of vehicles, various diseases, and biomedical entities) with limited training data for each type. The RUFEERS track challenges systems to recognize mentions of entities, events, and relations in news articles, from an ontology of approximately 55 event, 30 relation, and 350 entity types that cover a variety of topics. Participating systems will extract mentions of entities, events, and relations from the ontology, including the roles (if any) that the entities play in the events and relations.

The track will evaluate systems on three tasks. Given an ontology and a set of Washington Post articles:

  1. Task 1: Extract one mention of each event, relation, and event/relation argument from each document
  2. Task 2: Extract all mentions of events, relations, and their arguments from each document
  3. Task 3: Extract all mentions of each entity from each document

Organizing Committee

    Shudong Huang (U.S. National Institute of Standards and Technology,
    Hoa Trang Dang (U.S. National Institute of Standards and Technology,

NIST is an agency of the
U.S. Department of Commerce

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Last updated: Friday, 16-Feb-2024 12:38:46 MST
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