METHODS AND SYSTEMS FOR IMPROVING MACHINE LEARNING PERFORMANCE

• US 20160162569A1
• Filed: 12/09/2015
• Published: 06/09/2016
• Est. Priority Date: 12/09/2014
• Status: Abandoned Application

• Current Assignee: AI IP Investments Limited
• Original Assignee: AI IP Investments Limited
• Inventors: Erle, Schuyler D., Munro, Robert J., Callahan, Brendan D., Brenier, Jason, Tepper, Paul A., Long, Jessica D., Robinson, James B., Nair, Aneesh, Casbon, Michelle, Krawczyk, Stefan
• Application Number:
  US14/964,510
• Publication Number:
  US 20160162569A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/243 Natural language query formulation
• CPC Class Codes: G06F 16/24532 of parallel queries
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/288 Entity relationship models
• CPC Class Codes: G06F 16/3329 Natural language query formulation or dialogue systems
• CPC Class Codes: G06F 16/35 Clustering; Classification
• CPC Class Codes: G06F 16/367 Ontology
• CPC Class Codes: G06F 16/93 Document management systems
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 3/0482 Interaction with lists of selectable items, e.g. menus
• CPC Class Codes: G06F 40/137 Hierarchical processing, e.g. outlines
• CPC Class Codes: G06F 40/169 Annotation, e.g. comment data or footnotes
• CPC Class Codes: G06F 40/221 Parsing markup language streams streaming G06F40/149
• 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
• CPC Class Codes: G06F 40/42 Data-driven translation
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

Systems and methods are presented for providing improved machine performance in natural language processing. In some example embodiments, an API module is presented that is configured to drive processing of a system architecture for natural language processing. Aspects of the present disclosure allow for a natural language model to classify documents while other documents are being retrieved in real time. The natural language model and the documents are configured to be stored in a stateless format, which also allows for additional functions to be performed on the documents while the natural language model is used to continue classifying other documents.

Claims

1. A method for conducting natural language processing, the method comprising:

generating a natural language model by a natural language platform;

storing the natural language model in a first stateless format;

accessing a plurality of documents to be classified by the natural language model;

storing the plurality of documents in a second stateless format; and

classifying, by the natural language platform, at least one document among the plurality of documents while the at least one document is stored in the second stateless format using the natural language model while stored in the first stateless format.

2. The method of claim 1, wherein storing the natural language model in the first stateless format comprises storing the natural language model in a language agnostic format.

3. The method of claim 1, wherein storing the plurality of documents in a second stateless format comprises storing all configuration and auxiliary data used to process each document among the plurality of documents with a combination of said document and the natural language model.

4. The method of claim 1, further comprising performing an intelligent queuing operation on a subset of the documents within the plurality of documents while classifying the at least one document, wherein the subset of documents is distinct from the at least one document.

5. The method of claim 1, further comprising performing a discover topics operation to discover documents that are classified into a specified label while classifying the at least one document.

6. The method of claim 1, wherein:

accessing the plurality of documents to be classified by the natural language model comprises retrieving a subset of the plurality of documents from a database; and

classifying the at least one document occurs while retrieving the subset of the plurality of documents, wherein the at least one document is distinct from the subset of the plurality of documents.

7. The method of claim 1, wherein storing the natural language model in a stateless format comprises storing replicas of the natural language model each into a server among a plurality of parallelized servers.

20. The computer readable medium of claim 1, wherein storing the natural language model in a stateless format comprises storing replicas of the natural language model each into a server among a plurality of parallelized servers.

8. A natural language processing system comprising:

a plurality of server machines communicatively coupled in parallel, each of the plurality of servers comprising a memory and at least one processor, each of the plurality of servers configured to;

store, in said memory of said server, a replica of a natural language model in a first stateless format;

access a plurality of documents to be classified by said replica of the natural language model;

store the plurality of documents in a second stateless format; and

classify at least one document among the plurality of documents while the at least one document is stored in the second stateless format using said replica of the natural language model while stored in the first stateless format.

9. The system of claim 8, wherein storing the replica of the natural language model in the first stateless format comprises storing the replica natural language model in a language agnostic format.

10. The system of claim 8, wherein storing the plurality of documents in a second stateless format comprises storing all configuration and auxiliary data used to process each document among the plurality of documents with a combination of said document and the replica natural language model.

11. The system of claim 8, wherein each of the plurality of servers is further configured to perform an intelligent queuing operation on a subset of the documents within the plurality of documents while classifying the at least one document, wherein the subset of documents is distinct from the at least one document.

12. The system of claim 8, wherein each of the plurality of servers is further configured to perform a discover topics operation to discover documents that are classified into a specified label while classifying the at least one document.

13. The system of claim 8, wherein:

accessing the plurality of documents to be classified by the natural language model comprises retrieving a subset of the plurality of documents from a database; and

classifying the at least one document occurs while retrieving the subset of the plurality of documents, wherein the at least one document is distinct from the subset of the plurality of documents.

14. A non-transitory computer readable medium comprising instructions that, when executed by a process, cause the processor to perform operations comprising:

generating a natural language model;

storing the natural language model in a first stateless format;

accessing a plurality of documents to be classified by the natural language model;

storing the plurality of documents in a second stateless format; and

classifying at least one document among the plurality of documents while the at least one document is stored in the second stateless format using the natural language model while stored in the first stateless format.

15. The computer readable medium of claim 14, wherein storing the natural language model in the first stateless format comprises storing the natural language model in a language agnostic format.

16. The computer readable medium of claim 14, wherein storing the plurality of documents in a second stateless format comprises storing all configuration and auxiliary data used to process each document among the plurality of documents with a combination of said document and the natural language model.

17. The computer readable medium of claim 14, wherein the operations further comprise performing an intelligent queuing operation on a subset of the documents within the plurality of documents while classifying the at least one document, wherein the subset of documents is distinct from the at least one document.

18. The computer readable medium of claim 14, wherein the operations further comprise performing a discover topics operation to discover documents that are classified into a specified label while classifying the at least one document.

19. The computer readable medium of claim 14, wherein:

accessing the plurality of documents to be classified by the natural language model comprises retrieving a subset of the plurality of documents from a database; and

classifying the at least one document occurs while retrieving the subset of the plurality of documents, wherein the at least one document is distinct from the subset of the plurality of documents.