US 2018/0121413 A1Published application

System and method for extracting entities in electronic documents

AssigneeKira Inc.

InventorsRobert Henry WARREN, Alexander Karl HUDEK

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Abstract

A method for entity extraction within an electronic document including executing by a computer processor a conditional random field algorithm stored on a computer readable medium to generate a conditional random field model; the conditional random field algorithm having an input including one or more training text documents; executing by a computer processor an entity extraction algorithm stored on a computer readable medium to generate an entity extraction model; the entity extraction algorithm having an input including the same one or more training text documents input into the conditional random field algorithm; applying by a computer processor the conditional random field model to at least one electronic document; wherein application of the conditional random field model returns a list of passages in the at least one electronic document having an entity; applying by a computer processor the entity extraction model to the at least one electronic document; wherein application of the entity extraction model returns a list of entities; and storing the list of entities on a compute readable medium.

Claims10

Dependent claims

A method for entity extraction within an electronic document, wherein an entity may be a proper noun;

the method comprising:
executing by a computer processor a conditional random field algorithm stored on a computer readable medium to generate a conditional random field model;
the conditional random field algorithm having an input including one or more training text documents;
executing by a computer processor an entity extraction algorithm stored on a computer readable medium to generate an entity extraction model;
the entity extraction algorithm having an input including the same one or more training text documents input into the conditional random field algorithm;
applying by a computer processor the conditional random field model to at least one electronic document;
wherein application of the conditional random field model returns a list of passages in the at least one electronic document having an entity;
applying by a computer processor the entity extraction model to at least one electronic document;
wherein application of the entity extraction model returns a list of entities;
storing the list of entities on a compute readable medium;
wherein the conditional random field model is generated by deconstructing by a computer processor training electronic texts stored on a computer readable into complete sentences and tokens used by the text in each sentence, along with the sequence of sentences;
and identifying entities based on either their context within sentences or the tokens used by text in the sentences.

9 more elements

A system for extracting entities from an electronic document, wherein an entity may be a proper noun;

the system comprising:
a computer processor executing a conditional random field algorithm stored on a computer readable medium to generate a conditional random field model;
the conditional random field algorithm having an input including one or more training text documents;
a computer processor executing an entity extraction algorithm stored on a computer readable medium to generate an entity extraction model;
the entity extraction algorithm having an input including the same one or more training text documents input into the conditional random field algorithm;
a computer processor applying the conditional random field model to at least one electronic document;
wherein application of the conditional random field model returns a list of passages in the at least one electronic document having an entity;
a computer processor applying the entity extraction model to the at least one electronic document;
wherein application of the entity extraction model returns a list of entities;
a data storing for storing the list of entities on a compute readable medium;
wherein the conditional random field model is generated by deconstructing training electronic texts stored on a computer readable into complete sentences and tokens used by the text in each sentence, along with the sequence of sentences;
and identifying entities based on either their context within sentences or the tokens used by text in the sentences.

9 more elements

Dependencies are inferred from the claim text.