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.