Thomson Reuters Enterprise Centre Gmbh

Kanika Madan

Isabelle Moulinier

Tonya Custis

Gayle McElvain

Julian Brooke

Khaled Ammar

Afsaneh Fazly

Hector Martinez Alonso

Diane Erickson

Gayle Mcelvain

The present disclosure relates to systems and methods for analyzing citationally related content and identifying, based on the analysis, a risk of impliedly overruled content. Embodiments provide for receiving case law data from a document source, for extracting a case triple that includes a first case overruling or abrogating a second case, and a third case citationally related to the second case. Features may be generated from case triple, such as natural processing language features comparing the language in the various cases of the triple, and feeding the generated features to a main classifier. In embodiments, the main classifier classifies the case triple into a class indicating the risk probability that the second case is impliedly overruled by the first case.

Systems and methods for identifying a risk of impliedly overruled content based on citationally related content

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Toronto, Canada

Kanika Madan