Entity disambiguation resolves the many-to-many correspondence between mentions of entities in text and unique real-world entities. Entity disambiguation can bring to bear global (corpus-level) statistics to improve the performance of named entity recognition systems. More importantly, intelligence analysts are keenly interested in relationships between real-world entities. Entity disambiguation makes possible additional types of relation assertions and affects relation extraction performance assessment. Finally, link analysis and inference inherently operate at the level of entities, not text strings. Thus, entity disambiguation is a prerequisite to carrying out these higher-level operations on information extracted from plain text. This paper describes Fair Isaacs automatic entity disambiguation capability and its performance.
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