Semantic processor for recognition of cause-effect relations in natural language documents

• US 20060041424A1
• Filed: 10/24/2005
• Published: 02/23/2006
• Est. Priority Date: 07/31/2001
• Status: Active Grant

• Current Assignee: Allium US Holding LLC
• Original Assignee: IHS Global Incorporated (Markit Group Ltd.)
• Inventors: Sovpel, Igor, Todhunter, James, Pastanohau, Dzianis, Vorontsov, Alexander
• Granted Patent: US 9,009,590 B2
• Time in Patent Office: Days
• US Class Current: 704/9
• CPC Class Codes: G06F 16/345 Summarisation for human users
• CPC Class Codes: G06F 40/30 Semantic analysis

Abstract

A Semantic Processor for the recognition of Cause-Effect relations in natural language documents which includes a Text Preformatter, a Linguistic Analyzer and a Cause-Effect Knowledge Base Generator. The Semantic Processor provides automatic recognition of cause-effect relation both inside single fact and between the facts in arbitrary text documents, where the facts are also automatically extracted from the text in the form of seven-field semantic units. The recognition of Cause-Effect relations is carried out on the basis of linguistic (including semantic) text analysis and a number of recognizing linguistic models built in the form of patterns.

Claims

1. A method for recognizing cause-effect relations in natural language documents to generate a cause-effect knowledge base comprising:

providing text from at least one natural language document;

recognizing one or more expanded Subject-Action-Object (eSAO) sets in the text, wherein each eSAO set has one or more eSAO components;

matching the one or more eSAO sets against cause-effect relationship patterns;

generating one or more eSAO cause-effect relations based on the matching, wherein the eSAO cause-effect relation comprises a cause eSAO and an effect eSAO; and

storing the one or more eSAO cause-effect relations in a knowledge base.

2. The method according to claim 1, wherein the one or more eSAO components comprise text related to one or more elements of the group consisting of:

subjects, objects, actions, adjectives, prepositions, indirect objects, and adverbs.

3. The method according to claim 1, wherein the cause eSAO comprises one or more of the eSAO components of the one or more eSAO sets and the effect eSAO comprises one or more eSAO components of the one or more eSAO sets.

4. The method according to claim 2, wherein the eSAO cause-effect relation comprises a sequential operator relating the eSAO components of the cause eSAO to the eSAO components of the effect eSAO with lexical, grammatical, and/or semantic language means.

5. The method according to claim 1, further comprising:

applying parts-of-speech tags to at least portions of the text to generate tagged portions of the text; and

parsing the tagged portions of the text to generate parsed and tagged portions of the text, wherein recognizing the eSAO sets in the text is performed on the parsed and tagged portions of the text.

6. The method according to claim 4, wherein applying parts-of-speech tags is performed on preformatted portions of the text, whereby the preformatted portions of the text comprise the text with non natural language symbols removed.

7. The method according to claim 1, wherein matching the one or more eSAO sets against cause-effect relationship patterns comprises matching a single eSAO set and generating one or more eSAO cause-effect relations based on the matching comprises generating a single eSAO cause-effect relation.

8. The method according to claim 1, wherein matching the one or more eSAO sets against cause-effect relationship patterns comprises matching a pair of eSAO sets and generating one or more eSAO cause-effect relations based on the matching comprises generating a single eSAO cause-effect relation based on matching the pair.

9. The method according to claim 1, wherein matching the one or more eSAO sets against cause-effect relationship patterns comprises accessing a cause-effect pattern database, and wherein the cause-effect pattern database is generated by a method comprising:

recognizing eSAO sets in a corpus of text documents;

generating a corpus of sentences, wherein each sentence contains at least one of the recognized eSAO sets;

recognizing particular cases of cause-effect relations in the sentences;

generalizing the particular cases of cause-effect relations to eSAO cause-effect patterns;

storing the eSAO cause-effect patterns in a cause-effect pattern database.

10. The method according to claim 1, wherein recognizing one or more expanded Subject-Action-Object (eSAO) sets in the text comprises accessing a linguistic knowledge base having a database defining eSAO components.

11. The method according to claim 1, wherein recognizing one or more expanded Subject-Action-Object (eSAO) sets in the text comprises recognizing subjects, objects, actions, adjectives, prepositions, indirect objects, and/or adverbs in one or more sentences of the text.

12. A computer-readable medium having computer-executable instructions for performing the method according to claim 1.

13. A semantic processor for recognizing cause-effect relations in text in electronic or digital form comprising:

a linguistic knowledge base; and

a semantic analyzer comprising;

an expanded subject-action-object (eSAO) recognizer for producing one or more eSAO sets based on the text, wherein the eSAO sets are based on eSAO definitions stored in the linguistic knowledge base; and

a cause-effect recognizer for matching the one or more eSAO sets with known cause-effect relationship patterns stored in the linguistic knowledge base and producing one or more eSAO cause-effect relations based on the match,

whereby the one or more eSAO cause-effect relations are stored in a knowledge base for later use.

14. The semantic processor according to claim 13, wherein the semantic processor comprises a linguistic analyzer comprising the semantic analyzer and the linguistic analyzer further comprises:

a parts-of speech tagger for receiving preformatted text based on the text in electronic or digital format; and

a parser for receiving the text tagged by the parts-of-speech tagger and providing parsed text to the semantic analyzer,

wherein the parts-of-speech tagger and the parser operate with data stored in the linguistic knowledge base.

15. The semantic processor according to claim 14, further comprising:

a preformatter for receiving the text in electronic or digital format and producing preformatted text based on data stored in the linguistic knowledge base; and

a knowledge base generator for producing a knowledge base from the one or more eSAO cause-effect relations generated by the linguistic analyzer.

16. The semantic processor according to claim 15, wherein the preformatter performs at least one of the following functions:

removal of any symbols in a digital or electronic representation of the text that do not form part of natural language text;

detection and correction of any mismatches or mistakes in the text; and

partitioning the text into structures of sentences and words.

17. The semantic processor according to claim 13, wherein the known cause-effect relationship patterns are created by a cause-effect relationship generator comprising:

a corpus linguistic analyzer for recognizing eSAO sets in a corpus of text documents;

a corpus eSAO generator for generating a corpus of sentences, wherein each sentence contains at least one of the recognized eSAO sets;

a relation recognizer for recognizing particular cases of cause-effect relations in the sentences;

a pattern generator for generalizing the particular cases of cause-effect relations to eSAO cause-effect patterns; and

a pattern tester for storing the eSAO cause-effect patterns in a cause-effect pattern database, wherein the cause-effect pattern database is a database within the linguistic knowledge base.

18. The semantic processor according to claim 13, wherein the eSAO cause-effect relation comprises a cause eSAO, an effect eSAO, and at least one sequential operator relating the cause eSAO to the effect eSAO.

19. The semantic processor according to claim 18, wherein each eSAO set based on the text comprises eSAO components and the cause eSAO comprises one or more of the eSAO components and the effect eSAO comprises one or more of the eSAO components different than the one or more eSAO components of the cause eSAO.

20. The semantic processor according to claim 19, wherein the eSAO components comprise text related to one or more elements of the group consisting of:

subjects, objects, actions, adjectives, prepositions, indirect objects, and adverbs.

21. The semantic processor according to claim 13, wherein the cause-effect recognizer matches a single eSAO set with a known cause-effect relationship pattern and produces a single eSAO cause-effect relation.

22. The semantic processor according to claim 13, wherein the cause-effect recognizer matches a pair of eSAO sets with a known cause-effect relationship pattern and produces a single eSAO cause-effect relation.