FACILITATING INFORMATION EXTRACTION VIA SEMANTIC ABSTRACTION

• US 20160246779A1
• Filed: 02/23/2015
• Published: 08/25/2016
• Est. Priority Date: 02/23/2015
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Ho, Ching-Tien, Kimelfeld, Benny, Li, Yunyao, Vaithyanathan, Shivakumar
• Granted Patent: US 10,019,437 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 40/30 Semantic analysis

Abstract

A method includes receiving one or more natural language dependency parse trees as input. A hardware processor is used for processing the dependency parse trees by creating a mapping from nodes of the one or more dependency parse trees into actions, roles and contextual predicates. The mapping is used for information extraction. The actions include the verbs along with attributes of the verbs. The roles include arguments for the verbs. The contextual predicates include modifiers for the verbs.

Claims

1. A method comprising:

receiving one or more natural language dependency parse trees as input;

processing, using a hardware processor, the dependency parse trees by creating a mapping from nodes of the one or more dependency parse trees into actions, roles and contextual predicates; and

using the mapping for information extraction,

wherein;

the actions comprise said verbs along with attributes of said verbs;

the roles comprise arguments for said verbs; and

the contextual predicates include modifiers for said verbs.

2. The method of claim 1, wherein said processing further comprises:

identifying verbs and extracting verb attributes; and

associating the roles and context with said verbs.

3. The method of claim 2, wherein said processing further comprises:

identifying verb enclosures;

eliminating auxiliary verbs;

updating one or more relational language views for the input; and

outputting a collection of relational language views.

4. The method of claim 1, wherein the roles further comprise lemmata and determiners for said verbs, the attributes comprise one or more of mood, voice, tense, and verb basis, and the modifiers comprise one or more of adverbs, temporal modifiers, and location modifiers.

5. The method of claim 1, wherein for said processing, indices for verb categorization is used to determine the role names.

6. The method of claim 4, wherein a declarative path language is used for defining the mapping of the one or more dependency parse trees into the roles.

7. The method of claim 1, wherein said processing further comprises:

eliminating auxiliary verbs;

eliminating optional information from auxiliary verbs that is propagated into the action; and

exposing enclosure relationships among the actions.

8. The method of claim 1, wherein adverbs and explicit negation language are uniformly exposed as negative and semi-negative voice, and the contextual predicates are classified into types.

9. A computer program product for information extraction via semantic abstraction, the computer program product comprising a computer readable storage medium having program code embodied therewith, the program code readable/executable by a processor to:

receive one or more natural language dependency parse trees as input;

process, using the processor, the dependency parse trees by creating a mapping from nodes of the one or more dependency parse trees into actions, roles and contextual predicates; and

using the mapping for extracting information,

wherein;

the actions comprise said verbs along with attributes of said verbs;

the roles comprise arguments for said verbs; and

the contextual predicates include modifiers for said verbs.

10. The computer program product of claim 9, wherein said process further comprises:

identifying verbs and extracting verb attributes; and

associating the roles and context with said verbs.

11. The computer program product of claim 10, wherein said process further comprises:

identifying verb enclosures;

eliminating auxiliary verbs;

updating one or more relational language views for the input; and

outputting a collection of relational language views.

12. The computer program product of claim 9, wherein the roles further comprise lemmata and determiners for said verbs, the attributes comprise one or more of mood, voice, tense, and verb basis, and the modifiers comprise one or more of adverbs, temporal modifiers, and location modifiers.

13. The computer program product of claim 9, wherein for said processing, indices for verb categorization is used to determine the role names.

14. The computer program product of claim 12, wherein a declarative path language is used for defining the mapping of the one or more dependency parse trees into the roles.

15. The computer program product of claim 9, wherein said process further comprises:

eliminating auxiliary verbs;

eliminating optional information from auxiliary verbs that is propagated into the action; and

exposing enclosure relationships among the actions.

16. The computer program product of claim 9, wherein adverbs and explicit negation language are uniformly exposed as negative and semi-negative voice, and the contextual predicates are classified into types.

17. A system comprising:

a processor;

a storage device coupled to the processor, wherein the storage device stores one or more text files;

a parser that parses text into one or more natural language dependency parse trees;

an action process that uses the processor for processing the dependency parse trees by creating a mapping from nodes of the one or more dependency parse trees into actions, roles and contextual predicates, wherein;

the mapping is used for information extraction;

the actions comprise said verbs along with attributes of said verbs;

the roles comprise arguments for said verbs; and

the contextual predicates include modifiers for said verbs.

18. The system of claim 17, wherein said action process further comprises:

identifying verbs and extracting verb attributes;

associating the roles and context with said verbs;

identifying verb enclosures;

eliminating auxiliary verbs;

updating one or more relational language views for the input; and

outputting a collection of relational language views.

19. The system of claim 17, wherein the roles further comprise lemmata and determiners for said verbs, the attributes comprise one or more of mood, voice, tense, and verb basis, the modifiers comprise one or more of adverbs, temporal modifiers, and location modifiers, wherein for said processing, indices for verb categorization is used to determine the role names, and a declarative path language is used for defining the mapping of the one or more dependency parse trees into the roles.

20. The system of claim 17, wherein said action process further comprises:

eliminating auxiliary verbs;

eliminating optional information from auxiliary verbs that is propagated into the action; and

exposing enclosure relationships among the actions,

wherein adverbs and explicit negation language are uniformly exposed as negative and semi-negative voice, and the contextual predicates are classified into types.