Processing text with domain-specific spreading activation methods
First Claim
1. One or more non-transitory electronic memory devices including computer instructions for performing a method comprising using a central processing unit (CPU) to create, from a text of one or more documents containing natural language stored in a non-transitory electronic memory device, a semantic network having a plurality of nodes corresponding to sets of semantic and episodic concepts and weighted links between the sets of semantic and episodic concepts,the sets of semantic concepts consisting of a plurality of semantic concepts associated with a plurality of known words;
- the sets of episodic concepts consisting of a plurality of episodic concepts associated with the semantic concepts; and
utilizing, using the CPU, spreading activation algorithms to refine the weighted links in the semantic network.
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Abstract
A method for performing natural language processing of free text using domain-specific spreading activation. Embodiments of the present invention ontologize free text using an algorithm based on neurocognitive theory by simulating human recognition, semantic, and episodic memory approaches. Embodiments of the invention may be used to process clinical text for assignment of billing codes, analyze suicide notes or legal discovery materials, and for processing other collections of text. Further, embodiments of the invention may be used to more effectively search large databases, such as a database containing a large number of medical publications.
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Citations
21 Claims
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1. One or more non-transitory electronic memory devices including computer instructions for performing a method comprising using a central processing unit (CPU) to create, from a text of one or more documents containing natural language stored in a non-transitory electronic memory device, a semantic network having a plurality of nodes corresponding to sets of semantic and episodic concepts and weighted links between the sets of semantic and episodic concepts,
the sets of semantic concepts consisting of a plurality of semantic concepts associated with a plurality of known words; -
the sets of episodic concepts consisting of a plurality of episodic concepts associated with the semantic concepts; and utilizing, using the CPU, spreading activation algorithms to refine the weighted links in the semantic network. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 19, 20, 21)
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11. A-One or more non-transitory electronic memory devices including computer instructions for performing a method comprising using a CPU for processing a text of one or more documents containing natural language, the method comprising
tagging, using the CPU, parts of speech in the text; -
recognizing, using the CPU, known words in the text; and creating, using the CPU, a semantic network, the semantic network including at least one of the recognized known words and at least one relationship with at least one semantic concept associated with at least one of the recognized known words; and supplementing the semantic network by iteratively adding additional concepts and additional relationships to the semantic network until a termination requirement is met, each additional concept being associated with at least a prior one of the concepts and additional concepts in the semantic network by a respective additional relationship, at least one of the additional concepts being an episodic concept separate from the at least one semantic concept. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18)
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Specification