Intelligent Web search and autonomous agents responding to free text need detailed information about the entities mentioned and the relations they participate in. We have developed a number of tools to assign fine-grained entity types, link entities to Freebase, and extract relations between entities, including:
  • FIGER fine-grained entity recognizer assigns over 100 semantic types
  • VINICULUM identifies entity mentions in text and maps them to Freebase entities
  • MultiR learns relation extractors using distant supervision from Freebase
  • Information Omnivore learns relation extractors from a combination of crowd sourcing and distant supervision
  • A system to learn common-sense attributes objects (e.g. size & shape) through text mining & global inference.
  • NewsSpike, a system that automatically discovers and extracts events from news text.
We have also developed GoReCo , a new gold standard evaluation for relation extraction consisting of exhaustive annotations of the 128 documents from ACE 2004 newswire for 48 relations. The code and data are available at It requires the original ACE 2004 download from the Linguistics Data Consortium (