Domain-Specific NLP Command Processing via Corpus Matching
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Solution Overview
Problem
Natural language processing systems are not configured to process domain-specific commands, which require specific definitions of actions and entities within a particular domain, leading to an inability to understand commands like 'Pay my XYZ bill' or 'Invoice ABC for $500', as they cannot determine the intended actions or entities.
Innovation Solution
A domain-specific natural language processing system that uses a corpus of known commands annotated with domain-specific actions and entities to identify probabilistic matches in received commands, allowing it to execute specific actions on identified entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a natural language processing system is configured for general computing tasks, then it can process common commands like calendar appointments and navigation, but it cannot process domain-specific commands with specialized actions and entities
Solution Approach 1:
The system segments the natural language processing functionality into a general-purpose core and domain-specific modules. Each domain (e.g., finance, healthcare) has its own action and entity definitions that can be independently configured and activated, allowing the system to handle domain-specific commands without requiring complete system redesign.
Solution Approach 2:
The natural language processing system is designed with universal capability to process multiple types of commands through a single interface. The system maintains a unified command processing architecture that can accommodate both general computing tasks and domain-specific commands by dynamically loading appropriate domain definitions and action mappings.
2Reliability
If domain-specific actions and entities are introduced to enable specialized functions, then the system can process commands like 'Pay my XYZ bill' or 'Invoice ABC for $500', but the system complexity increases due to the need for domain-specific command corpora and mappings
Solution Approach 1:
The system performs preliminary action by pre-defining and pre-mapping domain-specific actions and entities before actual command processing occurs. Domain definitions are established in advance with explicit action-to-function mappings, allowing the system to accurately recognize and execute domain-specific commands without complex real-time analysis during command execution.
Solution Approach 2:
The system introduces intermediary structures including action corpora, entity vocabularies, and mapping tables that serve as mediators between the natural language input and the execution layer. These intermediaries translate domain-specific natural language commands into standardized internal representations, simplifying the overall processing architecture while maintaining high recognition accuracy.
Data Source
AI summary
The present disclosure relates to processing domain-specific natural language commands. An example method generally includes receiving a natural language command. A command processor compares the received natural language command to a corpus of known commands to identify a probable matching command in the corpus of known commands to the received natural language command. The corpus of known commands comprises a plurality of domain-specific commands, each of which is mapped to a domain-specific action. Based on the comparison, the command processor identifies the domain-specific action associated with the probable matching command to perform in response to the received command and executes the identified domain-specific action.