Patent Document ID: 9311301
Application ID: 14750936

Base Claim:
1. A computer-implemented method, comprising: ingesting text data from a plurality of documents containing a plurality of mentions; locating, from the text data, for each of a selected plurality of chains of coreferent mentions, a particular context-based name from the respective chain, wherein the coreferent mentions correspond to entities and the context-based name is a longest name in the respective chain, a last name in the respective chain, or a most frequently occurring name in the respective chain; determining an entity category for each respective one of the plurality of chains; determining one or more entity attributes from structured data and unstructured data; based on the located particular context-based name, the entity category, and the one or more attributes, assigning high-probability coreferent chains to high-confidence buckets, such as to produce a power law probability distribution having a head region and a tail region; and resolving, based at least in part on the power law probability distribution, the coreferent mentions to identify corresponding real-world entities.

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Claim 7:
7. The method of claim 1 , further comprising: determining overlap of incoming data with data in an existing coreference hierarchy by assigning the high-probability coreferent chains to the high-confidence buckets; performing computations on an overlapping region of the distribution that corresponds to the determined overlap, and restoring the computations from the previous state for the remainder of the head region and tail region; and performing best entity guesses for the newly-computed sub-entities in the overlapping region to aid resolution, the best entity guesses corresponding to an estimation of an entity to which each of the newly-computed sub-entities corresponds.