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

• US 20060009966A1
• Filed: 11/03/2004
• Published: 01/12/2006
• Est. Priority Date: 07/12/2004
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Johnson, David E., Oles, Frank J.
• Application Number:
  US10/979,162
• Publication Number:
  US 20060009966A1
• Time in Patent Office: Days
• US Class Current: 704/10
• CPC Class Codes: G06F 40/205 Parsing

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.

Claims

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.