Semantic network methods to disambiguate natural language meaning

• US 8,135,660 B2
• Filed: 10/10/2009
• Issued: 03/13/2012
• Est. Priority Date: 05/28/1998
• Status: Expired due to Fees

• Current Assignee: DataCloud Technologies, LLC (IP Investments Group LLC)
• Original Assignee: QPS Tech LLC (Intellectual Ventures LLC)
• Inventors: Au, Lawrence
• Primary Examiner(s): Gaffin, Jeffrey A
• Assistant Examiner(s): Tran, Mai T
• Application Number:
  US12/577,160
• Publication Number:
  US 20100030724A1
• Time in Patent Office: 885 Days
• Field of Search: 706/55, 704/2, 704/8
• US Class Current: 706/55
• CPC Class Codes: G06F 16/3344 using natural language analysis
• CPC Class Codes: G06F 40/237 Lexical tools
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G10L 15/1815 Semantic context, e.g. disambiguation of the recognition hypotheses based on word meaning

Abstract

A computer implemented data processor system automatically disambiguates a contextual meaning of natural language symbols to enable precise meanings to be stored for later retrieval from a natural language database, so that natural language database design is automatic, to enable flexible and efficient natural language interfaces to computers, household appliances and hand-held devices.

Claims

1. A non-transitory computer-readable medium comprising computer executable instructions that, in response to execution, cause a machine to perform comprising:

receiving a natural language input comprising a plurality of symbols;

storing the plurality of symbols as a plurality of input nodes in a semantic network, wherein at least one of the input nodes has polysemy, wherein a plurality of candidate meanings for the at least one of the input nodes are stored respectively as a plurality of candidate nodes in the semantic network, and wherein the semantic network includes a stored natural language context comprising a plurality of context nodes;

identifying a plurality of semantic links between nodes of the semantic network, wherein at least one of the plurality of semantic links traverses from one of the plurality of candidate nodes to one of the plurality of context nodes;

computing a contextual distance for two or more of the plurality of semantic links;

comparing two or more of the contextual distances to determine a contextual distance for the at least one of the input nodes; and

select at least one of the plurality of candidate nodes for the at least one of the input nodes, wherein the at least one selected candidate node has an associated contextual distance approximating the contextual distance for the at least one of the input nodes.

2. The non-transitory computer-readable medium of claim 1, wherein comparing two or more of the contextual distances to determine a contextual distance comprises:

determining a minimum average link distance between at least one of the plurality of candidate nodes and at least one of the plurality of context nodes.

3. The non-transitory computer-readable medium of claim 1, wherein the semantic network includes a plurality of semantic nodes, and wherein at least one of the selected candidate nodes inherits from a common semantic network node, the common semantic network node being selected from the plurality of semantic nodes.

4. The non-transitory computer-readable medium of claim 1, further comprising identifying no semantic links traversing from at least one of the plurality of candidate nodes to at least one of the plurality of context nodes for at least one of a plurality of semantic link types.

5. The non-transitory computer-readable medium of claim 4, wherein, in response to no semantic links being identified as traversing from at least one of the plurality of candidate nodes to at least one of the plurality of context nodes, estimating a distance from at least one of the plurality of candidate nodes to at least one of the plurality of context nodes.

6. The non-transitory computer-readable medium of claim 4, wherein at least one of the plurality of semantic links corresponds to at least one of a plurality of semantic link types.

7. The non-transitory computer-readable medium of claim 1, further comprising receiving the plurality of input nodes via voice recognition.

8. The non-transitory computer-readable medium of claim 1, wherein the machine is a household appliance.

9. The non-transitory computer-readable medium of claim 8, wherein the household appliance is a microwave oven.

10. The non-transitory computer-readable medium of claim 1, wherein the machine is a hand-held device.

11. The non-transitory computer-readable medium of claim 1, wherein the machine is a wireless communication device.

12. The non-transitory computer-readable medium of claim 1, further comprising performing one or more voice synthesis functions.

13. A computing device, comprising:

a natural language user interface configured to receive a natural language input,

a natural language processor configured to;

store the natural language input as a plurality of input nodes in a semantic network, wherein at least one of the input nodes has polysemy, wherein a plurality of candidate meanings for the at least one of the input nodes are stored respectively as a plurality of candidate nodes in the semantic network, and wherein the semantic network includes a stored natural language context comprising a plurality of context nodes;

identify a plurality of semantic links between nodes of the semantic network, wherein at least one of the plurality of semantic links traverses from one of the plurality of candidate nodes to one of the plurality of context nodes;

computer a contextual distance for two or more of the plurality of semantic links;

compare two or more of the contextual distances to determine a contextual distance for the at least one of the input nodes; and

select at least one of the plurality of candidate nodes for the at least one of the input nodes having polysemy, wherein the at least one selected candidate node has an associated contextual distance approximating the contextual distance for the at least one of the input nodes.

14. The computing device of claim 13, wherein the natural language processor is further configured to compare the two or more of the contextual distances to determine a contextual distance for the at least one of the input nodes having polysemy by determining a minimum average link distance between at least one of the plurality of candidate nodes and at least one of the plurality of context nodes.

15. The computing device of claim 13, wherein the computing device is a household appliance.

16. The computing device of claim 13, wherein the computing device is wireless communication apparatus.

17. A system, comprising:

means for receiving a natural language input;

means for storing the natural language input as a plurality of input nodes in a semantic network, wherein at least one of the input nodes has polysemy, wherein a plurality of candidate meanings for the at least one of the input nodes are stored respectively as a plurality of candidate nodes in the semantic network, and wherein the semantic network includes a stored natural language context comprising a plurality of context nodes;

means for identifying a plurality of semantic links between nodes of the semantic network, wherein at least one of the plurality of semantic links traverses from one of the plurality of candidate nodes to one of the plurality of context nodes;

means for computing a contextual distance for two or more of the plurality of semantic links;

means for comparing two or more of the contextual distances to determine a contextual distance for the at least one of the input nodes; and

means for selecting at least one of the plurality of candidate nodes for the at least one of the input nodes having polysemy, wherein the at least one selected candidate node has an associated contextual distance approximating the contextual distance for the at least one of the input nodes.

18. The system of claim 17, further comprising means for comparing the two or more of the contextual distances to determine a contextual distance by determining a minimum average link distance between at least one of the plurality of candidate nodes and at least one of the plurality of context nodes.

19. The system of claim 17, further comprising means for receiving the natural language input as at least one of a visual or audio input.

20. The system of claim 17, further comprising means for performing at least one of image processing on the visual input or voice recognition on the audio input.