SYSTEM AND METHOD FOR AUTOMATIC SEMANTIC LABELING OF NATURAL LANGUAGE TEXTS

• US 20100235165A1
• Filed: 03/12/2010
• Published: 09/16/2010
• Est. Priority Date: 03/13/2009
• Status: Active Grant

• Current Assignee: Allium US Holding LLC
• Original Assignee: Invention Machine Corporation (S&P Global Inc.)
• Inventors: Sovpel, Igor, Todhunter, James, Pastanohau, Dzianis
• Granted Patent: US 8,583,422 B2
• Time in Patent Office: Days
• US Class Current: 704/9
• CPC Class Codes: G06F 16/3329 Natural language query formulation or dialogue systems
• CPC Class Codes: G06F 40/35 Discourse or dialogue representation

Abstract

Systems and methods for automatic semantic labeling of natural language documents provided in electronic or digital form include a semantic processor that performs a basic linguistic analysis of text, including recognizing in the text semantic relationships of the type objects and/or classes of objects, facts and cause-effect relationships; matching linguistically analyzed text against target semantic relationship patterns, created by generalization of particular cases of target semantic relationships; and generating semantic relationship labels based on linguistically analyzed text and a result of the matching.

Claims

1. A method for automatic labeling of natural language text, the method comprising:

providing at least one computer processor coupled to at least one non-transitory storage medium, the at least one computer processor performing the method, including;

receiving text from at least one natural language document in electronic form;

performing a basic linguistic analysis of the text;

matching the linguistically analyzed text against stored target semantic relationship patterns;

producing semantically labeled text by generating semantic relationship labels based on the linguistically analyzed text and a result of the matching of the linguistically analyzed text against the target semantic relationship patterns, wherein the semantic relationship labels are associated with words or phrases from sentences within the text and indicate components of predetermined types of semantic relationships; and

storing the semantically labeled text in a database.

2. 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;

parsing the tagged portions of the text to generate parsed and tagged portions of the text; and

semantically analyzing the parsed and tagged portions of the text to generate semantically analyzed, parsed and tagged portions of the text.

3. The method according to claim 2, 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.

4. The method according to claim 2, wherein semantically analyzing the parsed and tagged portions of the text comprises:

recognizing one or more facts in the form of at least one expanded Subject-Action-Object (eSAO) set in the text, wherein each eSAO set has at least one eSAO component; and

recognizing in the text a set of rules that reflect regularities of at least one of an external domain and a knowledge domain in the form of cause-effect relationships in at least one eSAO set, wherein at least one cause-effect relationship of the cause-effect relationships comprises a cause eSAO and an effect eSAO.

5. The method according to claim 4, wherein the at least one eSAO component comprises text related to one or more elements selected from the group consisting of subjects, objects, actions, adjectives, prepositions, indirect objects, and adverbs.

6. The method according to claim 4, wherein recognizing one or more expanded Subject-Action-Object (eSAO) sets in the text comprises recognizing one or more subjects, objects, actions, adjectives, prepositions, indirect objects, and adverbs in at least one sentence of the text.

7. The method according to claim 4, wherein recognizing one or more expanded Subject-Action-Object (eSAO) sets and cause-effect relationships in the text comprises accessing a linguistic knowledge base having a database of patterns defining eSAO and cause-effect components.

8. The method according to claim 4, wherein the cause eSAO comprises at least one eSAO component of the at least one eSAO set and the effect eSAO comprises at least one other eSAO component of the at least one eSAO set.

9. The method according to claim 8, wherein the at least one cause-effect relationship comprises a sequential operator relating the at least one eSAO component of the cause eSAO to the at least one other eSAO component of the effect eSAO with lexical, grammatical, and/or semantic language means.

10. The method according to claim 1, wherein matching the linguistically analyzed text against target semantic relationship patterns further comprises:

accessing a pattern database that is a part of a linguistic knowledge database, wherein the pattern database is generated by;

performing a basic linguistic analysis of a corpus of text documents;

recognizing in the linguistically analyzed corpus particular cases of target semantic relationships;

generalizing the particular cases of target semantic relationships into linguistic patterns using lexical language units and their semantic classes, part-of-speech and syntactic tags, eSAO and cause-effect labels from the recognized particular cases of target semantic relationships; and

storing the linguistic patterns.

11. The method according to claim 10, wherein generalizing the particular cases of target semantic relationships into linguistic patterns uses an eSAO format as a context, and comprises:

generalizing constituent components of the particular cases of target semantic relationships by searching in the linguistically analyzed corpus of text documents using lexical, grammatical, syntactic, eSAO and cause-effect labels obtained for the components from the basic linguistic analysis.

12. The method according to claim 1, wherein matching the linguistically analyzed text against the target semantic relationship patterns comprises matching words, part-of-speech tags, syntactic tags, eSAO, and cause-effect sets, wherein generating the semantic relationship labels comprises generating eSAO and cause-effect labels.

13. A computer program product comprising a computer-readable medium having computer-executable instructions that perform a method for semantic labeling of natural language texts when executed by at least one processor, the method comprising:

receiving text from at least one natural language document;

performing a basic linguistic analysis of the text;

matching the linguistically analyzed text against stored target semantic relationship patterns;

producing semantically labeled text by generating semantic relationship labels based on the linguistically analyzed text and a result of the matching of the linguistically analyzed text against the target semantic relationship patterns, wherein the semantic relationship labels are associated with words or phrases from sentences within the text and indicate components of predetermined types of semantic relationships; and

storing the semantically labeled text in a database.

14. A semantic processor for automatically semantic labeling of natural language text in electronic or digital form, the semantic processor comprising:

a preformatter that preformats received electronic text;

a linguistic analyzer that performs basic linguistic analysis of the preformatted text;

a labeler that matches the linguistically analyzed text against stored target semantic relationship patterns to produce semantically labeled text, wherein the semantically labeled text includes semantic relationship labels associated with words or phrases from sentences within the text that indicate components of predetermined types of semantic relationships.

15. The semantic processor according to claim 14, wherein the linguistic analyzer comprises a semantic analyzer that produces semantically analyzed text, the semantic analyzer comprising:

an expanded Subject-Action-Object (eSAO) recognizer that recognizes eSAOs sets in the text; and

a cause-effect (C-E) recognizer that recognizes a cause-effect relationship,

wherein eSAO and C-E recognition is based on linguistic patterns stored in a linguistic knowledge base.

16. The semantic processor according to claim 15, wherein the semantic relationship labels generated by the labeler comprise eSAO labels and cause-effect labels.

17. The semantic processor according to claim 15, wherein the eSAO cause-effect relationship comprises a cause eSAO, an effect eSAO, and at least one sequential operator relating the cause eSAO to the effect eSAO.

18. The semantic processor according to claim 17, wherein each eSAO set comprises eSAO components and the cause eSAO comprises at least one eSAO component of the eSAO components and the effect eSAO comprises at least one eSAO component of the eSAO components that is different from the at least one eSAO component of the cause eSAO.

19. The semantic processor according to claim 14, wherein the eSAO components comprise text related to one or more elements selected from the group consisting of subjects, objects, actions, adjectives, prepositions, indirect objects and adverbs.

20. The semantic processor according to claim 15, wherein the linguistic analyzer further comprises:

a part-of-speech (POS) tagger that receives the preformatted text and produces POS tagged text; and

a parser that receives the POS tagged text, produces parsed text, and provides the parsed text to the semantic analyzer,

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

21. The semantic processor according to claim 14, wherein the preformatter performs at least one of a removal of any symbols in a digital or electronic presentation of the text that do not form part of natural language text, a detection and correction of any mismatches or mistakes in the text, and partitioning the text into structures of sentences and words.

22. The semantic processor according to claim 14, wherein the target semantic relationship patterns are created by a pattern generator comprising:

a corpus linguistic analyzer that performs basic linguistic analysis of a corpus of text documents;

a labeled text corpus generator that generates a labeled text corpus having part-of-speech tags, syntactic tags, eSAO labels, and cause-effect labels;

a relation generator that recognizes in the labeled text corpus particular cases of target semantic relationships;

a pattern generator that generalizes the particular cases of semantic relationships by using their labels to generate more general linguistic patterns, wherein the labels include lexical language units, their semantic classes, part-of-speech and syntactic tags, and eSAO and cause-effect labels; and

a pattern tester for testing the general linguistic patterns by the pattern generator.

23. The semantic processor according to claim 22, wherein when generalizing the particular cases of target semantic relationships into linguistic patterns, the pattern generator uses an eSAO format as a context to:

generalize constituent components as a result of searching in the linguistically analyzed corpus of text documents using of part-of-speech, syntactic, and eSAO and cause-effect labels obtained for the components at a level of the basic linguistic analysis.

24. The semantic processor according to claim 14, wherein the labeler matches the linguistically analyzed text against target semantic relationship patterns by matching words, part-of-speech tags, syntactic tags, eSAO and cause-effect sets.