System and method for analyzing ambiguities in language for natural language processing

• US 8,949,170 B2
• Filed: 03/10/2014
• Issued: 02/03/2015
• Est. Priority Date: 09/24/2011
• Status: Active Grant

• Current Assignee: Z Advanced Computing, Inc.
• Original Assignee: Z Advanced Computing, Inc.
• Inventors: Zadeh, Lotfi A.
• Primary Examiner(s): Chaki, Kakali
• Assistant Examiner(s): Seck, Ababacar
• Application Number:
  US14/201,974
• Publication Number:
  US 20140188462A1
• Time in Patent Office: 330 Days
• Field of Search: None
• US Class Current: 706/48
• CPC Class Codes: G05B 13/0275 using fuzzy logic only
• CPC Class Codes: G06F 16/90344 by using string matching techniques
• CPC Class Codes: G06F 18/24 Classification techniques
• CPC Class Codes: G06F 40/253 Grammatical analysis; Style critique
• CPC Class Codes: G06F 40/40 Processing or translation of natural language natural language analysis G06F40/20; semantic analysis G06F40/30
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06N 5/046 Forward inferencing; Production systems
• CPC Class Codes: G06N 5/047 Pattern matching networks; Rete networks
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06N 7/02 using fuzzy logic computing arrangements based on biological models G06N3/00; computing arrangements using knowledge-based models G06N5/00
• CPC Class Codes: G06N 7/023 Learning or tuning the parameters of a fuzzy system
• CPC Class Codes: G06T 7/12 Edge-based segmentation

Abstract

Specification covers new algorithms, methods, and systems for artificial intelligence, soft computing, and deep learning/recognition, e.g., image recognition (e.g., for action, gesture, emotion, expression, biometrics, fingerprint, facial, OCR (text), background, relationship, position, pattern, and object), large number of images (“Big Data”) analytics, machine learning, training schemes, crowd-sourcing (using experts or humans), feature space, clustering, classification, similarity measures, optimization, search engine, ranking, question-answering system, soft (fuzzy or unsharp) boundaries/impreciseness/ambiguities/fuzziness in language, Natural Language Processing (NLP), Computing-with-Words (CWW), parsing, machine translation, sound and speech recognition, video search and analysis (e.g. tracking), image annotation, geometrical abstraction, image correction, semantic web, context analysis, data reliability (e.g., using Z-number (e.g., “About 45 minutes; Very sure”)), rules engine, control system, autonomous vehicle, self-diagnosis and self-repair robots, system diagnosis, medical diagnosis, biomedicine, data mining, event prediction, financial forecasting, economics, risk assessment, e-mail management, database management, indexing and join operation, memory management, and data compression.

Claims

1. A method for analyzing ambiguities in language for natural language processing, said method comprising:

an input device receiving a first sentence or phrase from a source;

wherein a vocabulary database stores words or phrases;

wherein a language grammar template database stores language grammar templates;

an analyzer module segmenting said first sentence or phrase, using words or phrases obtained from said vocabulary database and language grammar templates obtained from said language grammar template database;

said analyzer module parsing said first sentence or phrase into one or more sentence or phrase components;

said analyzer module determining Z-valuation for said one or more sentence or phrase components as a value of an attribute for said one or more sentence or phrase components;

wherein said Z-valuation for said one or more sentence or phrase components are based on one or more parameters with unsharp class boundary or fuzzy membership function;

said analyzer module processing language ambiguities in said first sentence or phrase for natural language processing, using said Z-valuation for said one or more sentence or phrase components.

2. The method for analyzing ambiguities in language for natural language processing as recited in claim 1, wherein said method comprises:

applying a Z-rule.

3. The method for analyzing ambiguities in language for natural language processing as recited in claim 1, wherein said method comprises:

applying a fuzzy modifier.

4. The method for analyzing ambiguities in language for natural language processing as recited in claim 1, wherein said method comprises:

applying a rules engine.

5. The method for analyzing ambiguities in language for natural language processing as recited in claim 1, wherein said method comprises:

responding to a query.

6. The method for analyzing ambiguities in language for natural language processing as recited in claim 1, wherein said method comprises:

receiving said first sentence or phrase from a search engine module.

7. The method for analyzing ambiguities in language for natural language processing as recited in claim 1, wherein said method comprises:

communicating with a translation module.

8. The method for analyzing ambiguities in language for natural language processing as recited in claim 1, wherein said method comprises:

applying an inference engine.

9. The method for analyzing ambiguities in language for natural language processing as recited in claim 1, wherein said method comprises:

applying a correlation analysis.

10. The method for analyzing ambiguities in language for natural language processing as recited in claim 1, wherein said method comprises:

applying a context analysis.

11. The method for analyzing ambiguities in language for natural language processing as recited in claim 1, wherein said method comprises:

recognizing a person'"'"'s emotion.

12. The method for analyzing ambiguities in language for natural language processing as recited in claim 1, wherein said method comprises:

applying a similar-sound database.

13. The method for analyzing ambiguities in language for natural language processing as recited in claim 1, wherein said method comprises:

applying a similar-spelling database.

14. The method for analyzing ambiguities in language for natural language processing as recited in claim 1, wherein said method comprises:

replacing neighboring keys for spelling correction.

15. The method for analyzing ambiguities in language for natural language processing as recited in claim 1, wherein said method comprises:

applying a coarse recognition.

16. The method for analyzing ambiguities in language for natural language processing as recited in claim 1, wherein said method comprises:

applying an expert input.

17. The method for analyzing ambiguities in language for natural language processing as recited in claim 1, wherein said method comprises:

converting to text or converting to voice.

18. The method for analyzing ambiguities in language for natural language processing as recited in claim 1, wherein said method comprises:

predicting a person'"'"'s behavior or taste.

19. A method for analyzing ambiguities in language for natural language processing, said method comprising:

an input device receiving a first sentence or phrase from a source;

wherein a vocabulary database stores words or phrases;

wherein a language grammar template database stores language grammar templates;

an analyzer module segmenting said first sentence or phrase, using words or phrases obtained from said vocabulary database and language grammar templates obtained from said language grammar template database;

said analyzer module parsing said first sentence or phrase into one or more sentence or phrase components;

said analyzer module determining Z-valuation for said one or more sentence or phrase components as a value of an attribute for said one or more sentence or phrase components;

wherein said Z-valuation for said one or more sentence or phrase components have parameters or attributes with soft boundaries;

said analyzer module processing language ambiguities in said first sentence or phrase for natural language processing, using said Z-valuation for said one or more sentence or phrase components.

20. A method for analyzing ambiguities in language for natural language processing, said method comprising:

an input device receiving a query from a source;

an analyzer module receiving said query from said input device;

said analyzer module receiving a first sentence or phrase;

said analyzer module segmenting said first sentence or phrase, using a semantic web or network;

said analyzer module parsing said first sentence or phrase into one or more sentence or phrase components;

said analyzer module determining Z-valuation for said one or more sentence or phrase components as a value of an attribute for said one or more sentence or phrase components;

wherein said Z-valuation for said one or more sentence or phrase components have parameters or attributes with soft boundaries;

said analyzer module responding to said query, using or based on said Z-valuation for said one or more sentence or phrase components.