Method and system for extracting information from unstructured text using symbolic machine learning
First Claim
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1. A method of extracting information from text, said method comprising:
- parsing an input sample of text to form a parse tree; and
receiving user inputs to define a machine-labeled learning pattern from said parse tree.
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Abstract
A method (and structure) of extracting information from text, includes parsing an input sample of text to form a parse tree and using user inputs to define a machine-labeled learning pattern from the parse tree.
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Citations
22 Claims
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1. A method of extracting information from text, said method comprising:
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parsing an input sample of text to form a parse tree; and
receiving user inputs to define a machine-labeled learning pattern from said parse tree. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. An apparatus for relational learning, said apparatus comprising:
a generator for developing a precedence inclusion (PI) pattern of a learning sample, wherein elements in said learning sample are machine-labeled to define a precedence relation and an inclusion relation. - View Dependent Claims (14, 15, 16)
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17. A signal-bearing medium tangibly embodying a program of machine-readable instructions executable by a digital processing apparatus to perform a method of relational learning, said machine-readable instructions comprising:
a precedence inclusion (PI) pattern learning module for generating a PI pattern of a learning sample wherein elements in said learning sample are machine-labeled to define a precedence relation and an inclusion relation. - View Dependent Claims (18, 19)
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20. A method of searching unseen text, said method comprising at least one of:
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conducting a search of unseen text by developing a precedence inclusion (PI) pattern of at least one learning sample and using said PI pattern for comparison with unseen text; and
providing a computerized tool to a user for said conducting said search.
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21. An apparatus for extracting information from text, said apparatus comprising:
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means for parsing an input sample of text to form a parsed tree; and
means for receiving user inputs to define a machine-labeled learning pattern from said parsed tree.
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22. A computerized tool for extracting information from text, said computerized tool comprising:
a precedence inclusion (PI) pattern learning module for generating a PI pattern of a learning sample wherein elements in said learning sample are machine-labeled to define a precedence relation and an inclusion relation.
Specification