Domain-Specific Natural-Language Processing Engine

• US 20130311166A1
• Filed: 10/12/2012
• Published: 11/21/2013
• Est. Priority Date: 05/15/2012
• Status: Abandoned Application

• Current Assignee: Andre Yanpolsky
• Original Assignee: Andre Yanpolsky
• Inventors: Yanpolsky, Andre
• Application Number:
  US13/650,132
• Publication Number:
  US 20130311166A1
• Time in Patent Office: Days
• US Class Current: 704/2
• CPC Class Codes: G06F 16/243 Natural language query formulation
• CPC Class Codes: G06F 16/3329 Natural language query formulation or dialogue systems
• CPC Class Codes: G06F 8/30 Creation or generation of source code

Abstract

The present disclosure provides a construction for managing domain specific, configurable natural-language processing. The system described allows for the extraction of entities and other discrete grammar components through a collection of iterative rulesets. Each instance of the parser system may be tailored to the domain of a particular subject of inquiry. Instance-level constraints enable increasingly fine classification on input data. Intuitive rulesets enable instance-level configuration by non-technical clients. A configured instance of the system receives unstructured text inputs and outputs structured data relevant to the domain of the instance.

Claims

1. A system for converting textual natural language commands and queries into a computer-readable, well-structured form, the system comprising:

an input for natural language commands or queries;

a set of text processing rules defining how the input will be interpreted;

a parser configured to apply the test processing rules to the input; and

an output to convey a set of structured data provided by the parser.

2. The system of claim 1, wherein the input has multiple interpretations outside of a single domain context.

3. The system of claim 1, wherein the text processing rules are domain specific.

4. The system of claim 1, wherein the text processing rules are defined and stored in a rules management environment.

5. The system of claim 1, wherein the text processing rules define how a set of structural elements will be extracted from the input by matching fragments of text against a predefined set of patterns and replacing these fragments with the structural elements.

6. The system of claim 1, wherein the parser is configured to process lexical information and apply the text processing rules to the input.

7. The system of claim 1, wherein the parser comprises:

a tokenization phase;

a lexical phase; and

a semantic phase.

8. The system of claim 1, wherein the structured output data can be written in any common programming language.

9. A method for converting textual natural language commands and queries into a computer-readable, well-structured form, the method comprising:

receiving a natural language command or query from an input;

retrieving a text processing rules set from a rules processing environment;

parsing the natural language command or query;

applying the selected text processing rules set to the parsed natural language command or query; and

rendering a structured-data output.

10. The system of claim 10, wherein the parsing step includes/comprises:

breaking the natural language command or query into tokens;

analyzing and correcting the tokens;

detecting topic signals based on pattern matching; and

rewriting to resolve semantic ambiguities.

11. The system of claim 10, wherein the rules are applied in an ordered, iterative fashion, wherein the system applies rules until no more rules may be applied.

12. A method for converting textual natural language commands and queries into a computer-readable, well-structured form, the method comprising:

receiving a natural language command or query from an input;

retrieving a test processing rules set from a rules processing environment;

breaking the natural language command or query into tokens;

analyzing and correcting the tokens;

detecting topic signals based on pattern matching;

rewriting to resolve semantic ambiguities;

applying the selected text processing rules set to the parsed natural language command or query; and

rendering a structured-data output.

13. The method of claim 12, wherein tokens are analyzed and corrected by first identifying the origin language of the tokens, then selecting the language-specific spell correct dictionary, then applying that selected dictionary to make the necessary corrections.

14. The method of claim 12, wherein the topic signals are detected through applying topic-detecting rulesets.

15. The method of claim 12, wherein the structured-data output is rendered as structured data in any common programming language.