Situation aware NLU/NLP

• US 9,619,459 B2
• Filed: 10/01/2012
• Issued: 04/11/2017
• Est. Priority Date: 10/01/2012
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Nuance Communications, Inc. (Microsoft Corporation)
• Inventors: Hebert, Matthieu, Robichaud, Jean-Philippe, Parisien, Christopher
• Primary Examiner(s): Desir, Pierre-Louis
• Assistant Examiner(s): Tzeng, Forrest F
• Application Number:
  US13/632,380
• Publication Number:
  US 20140095147A1
• Time in Patent Office: 1,653 Days
• Field of Search: 704 9
• CPC Class Codes: G06F 16/90332 Natural language query formulation or dialogue systems
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06F 40/40 Processing or translation of natural language natural language analysis G06F40/20; semantic analysis G06F40/30

Abstract

An arrangement and corresponding method are described for natural language processing. A natural language understanding (NLU) arrangement processes a natural language input to determine a corresponding sentence-level interpretation. A user state component maintains user context data that characterizes an operating context of the NLU arrangement. Operation of the NLU arrangement is biased by the user context data.

Claims

1. A system for natural language processing comprising:

a computing device that receives natural language input from a user;

a data store storing a user state model comprising user context data that characterizes an operating context of the computing device;

memory storing instructions that, when executed by a processor of the computing device, cause the system to (a) perform a natural language understanding (NLU) process that produces a final interpretation of the natural language input and (b) bias performance of the NLU process using the user context data of the user state model;

wherein the NLU process comprises;

processing the natural language input in parallel using a plurality of domain pipelines to produce, by each of the plurality of domain pipelines, one or more domain interpretation candidates, each domain pipeline representing a different subject domain of related concepts,

ranking each of the domain interpretation candidates produced by the plurality of domain pipelines, and

selecting, based on the ranking, one of the domain interpretation candidates produced by the plurality of domain pipelines as the final interpretation of the natural language input; and

wherein each domain pipeline comprises a plurality of stages, the plurality of stages comprising;

a mention detection stage that assigns, to one or more words of the natural language input, a tag indicating a semantic concept of the subject domain represented by the domain pipeline,

an interpretation stage that produces the one or more domain interpretation candidates, each domain interpretation candidate having a confidence value and the one or more domain interpretation candidates being ranked according to a confidence value for each domain interpretation candidate,

a query intent classification stage that determines a query intent for the natural language input, and

an evidence ranking stage that re-ranks the one or more domain interpretation candidates using a classifier trained on data representing the query intent.

2. A system according to claim 1, wherein the NLU process uses semantic templates for processing the natural language input.

3. A system according to claim 1, wherein the NLU process uses both rule-based NLU and statistical-based NLU for processing the natural language input.

4. A system according to claim 1, wherein the computing device is a mobile computing device that develops the natural language input.

5. A system according to claim 1, wherein the NLU process processes the natural language input in real-time.

11. The system of claim 1 wherein the natural language input received from the user is natural language speech input.

13. The system according to claim 1, wherein the plurality of stages of each domain pipeline further comprises a semantic attachment stage that attaches, to a mention of the natural language input, additional semantic information associated with the mention.

14. The system according to claim 1, wherein biasing performance of the NLU process comprises biasing the ranking of each of the domain interpretation candidates produced by the plurality of domain pipelines.

15. The system according to claim 1, wherein biasing performance of the NLU process comprises biasing at least one stage of the plurality of stages of each domain pipeline.

16. The system according to claim 1, wherein the ranking of each of domain interpretation candidates produced by the plurality of domain pipelines uses a classifier trained on data associated with the subject domains represented, respectively, by the plurality of domain pipelines.

6. A method for natural language processing comprising:

storing, at a data store of a computing device, a user state model comprising user context data that characterizes an operating context of the computing device;

receiving, at the computing device, natural language input from the user;

performing, by a processor of the computing device, a natural language understanding (NLU) process that produces a final interpretation of the natural language input;

wherein performance of the NLU process is biased using the user context data of the user state model;

wherein the NLU process comprises;

processing the natural language input in parallel using a plurality of domain pipelines to produce, by each of the plurality of domain pipelines, one or more domain interpretation candidates, each domain pipeline representing a different subject domain of related concepts,

ranking each of the domain interpretation candidates produced by the plurality of domain pipelines, and

selecting, based on the ranking, one of the domain interpretation candidates produced by the plurality of domain pipelines as the final interpretation of the natural language input; and

wherein each domain pipeline comprises a plurality of stages, the plurality of stages comprising;

a mention detection stage that assigns, to one or more words of the natural language input, a tag indicating a semantic concept of the subject domain represented by the domain pipeline,

an interpretation stage that produces the one or more domain interpretation candidates, each domain interpretation candidate having a confidence value and the one or more domain interpretation candidates being ranked according to a confidence value for each domain interpretation candidate,

a query intent classification stage that determines a query intent for the natural language input, and

an evidence ranking stage that re-ranks the one or more domain interpretation candidates using a classifier trained on data representing the query intent.

7. A method according to claim 6, wherein the NLU process processes the natural language input based on use of semantic templates.

8. A method according to claim 6, wherein the NLU process processes the natural language input using both rule-based NLU and statistical-based NLU.

9. A method according to claim 6, wherein the computing device is a mobile computing device and the natural language input is developed by the mobile device.

10. A method according to claim 6, wherein the NLU process processes the natural language input in real-time.

12. The method of claim 6 wherein the natural language input received from the user is natural language speech input.

17. The method according to claim 6, wherein the plurality of stages of each domain pipeline further comprises a semantic attachment stage that attaches, to a mention of the natural language input, additional semantic information associated with the mention.

18. The method according to claim 6, wherein biasing performance of the NLU process comprises biasing the ranking of each of the domain interpretation candidates produced by the plurality of domain pipelines.

19. The method according to claim 6, wherein biasing performance of the NLU process comprises biasing at least one stage of the plurality of stages of each domain pipeline.

20. The method according to claim 6, wherein the ranking of each of domain interpretation candidates produced by the plurality of domain pipelines uses a classifier trained on data associated with the subject domains represented, respectively, by the plurality of domain pipelines.