Method and system for extracting information from unstructured text using symbolic machine learning

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.

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.    
     
     
         2 . The method of  claim 1 , further comprising: 
 calculating a generalization of said learning pattern that is not also a parse tree.    
     
     
         3 . The method of  claim 1 , wherein machine-labeled learning pattern comprises a precedence inclusion pattern wherein elements in said learning pattern are defined in a precedence relation and in an inclusion relation.  
     
     
         4 . The method of  claim 3 , wherein said input sample comprises a first input sample, said parse tree comprises a first parse tree, and said learning pattern comprises a first learning pattern, said method further comprising: 
 parsing at least one more input sample of text to form therefrom a parse tree;    for each said at least one more input sample parse tree, defining therefrom a learning pattern; and    calculating a generalization of said learning patterns.    
     
     
         5 . The method of  claim 4 , wherein each said learning pattern comprises a precedence inclusion pattern wherein elements in said learning pattern are defined in a precedence relation and in an inclusion relation.  
     
     
         6 . The method of  claim 5 , wherein said generalization also comprises a precedence inclusion pattern.  
     
     
         7 . The method of  claim 6 , wherein said precedence inclusion pattern of said generalization comprises a most specific generalization (MSG).  
     
     
         8 . The method of  claim 7 , further comprising: 
 calculating a Minimal Most Specific Generalization (MMSG) of all of said learning samples.    
     
     
         9 . The method of  claim 2 , further comprising: 
 comparing said learning pattern with an unknown text.    
     
     
         10 . The method of  claim 8 , further comprising: 
 comparing said learning patterns with an unknown text    
     
     
         11 . The method of  claim 10 , wherein said comparing comprises: 
 parsing each said unknown text to form a parse tree;    calculating a generalization of said parse tree, said generalization forming a precedence inclusion pattern; and    using said MMSG to calculate a similarity of said unknown text to said learning patterns.    
     
     
         12 . The method of  claim 9 , wherein said comparing comprises: 
 parsing each said unknown text to form a parse tree;    calculating a generalization of said parse tree, said generalization forming a precedence inclusion pattern; and    calculating a similarity of said generalization of said parse tree of said unknown text with said generalization of said learning pattern.    
     
     
         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.    
     
     
         14 . The apparatus of  claim 13 , further comprising: 
 a graphical user interface (GUI) to permit a user to provide inputs used for said developing said PI pattern.    
     
     
         15 . The apparatus of  claim 13 , further comprising: 
 a comparison module for applying said PI pattern to unseen text and determining a similarity therebetween.    
     
     
         16 . The apparatus of  claim 15 , wherein said generator further calculates a Minimal Most Specific Generalization (MMSG) of all learning samples entered and said comparison is based on said MMSG.  
     
     
         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.    
     
     
         18 . The signal-bearing medium of  claim 17 , further comprising: 
 a graphical user interface (GUI) to permit a user to provide inputs to define said PI pattern for each said learning sample.    
     
     
         19 . The signal-bearing medium of  claim 17 , wherein said PI pattern learning module further calculates a Minimal Most Specific Generalization (MMSG) of all learning samples entered, said machine-readable instructions further comprising: 
 a PI application module for comparing unseen text with said learning samples, said comparing based on said MMSG.    
     
     
         20 . A method of searching unseen text, said method comprising at least one of: 
 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.    
     
     
         21 . An apparatus for extracting information from text, said apparatus comprising: 
 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.    
     
     
         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.