Semantic Parser for Natural Language Concept Construction
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Solution Overview
Problem
Current computer systems are unable to understand the meaning of human language effectively, as they lack the necessary theory and technology to interpret natural language, manage concept ellipsis, and handle nuances like deixis, metaphors, and ambiguity, making it difficult to communicate sensibly with humans.
Innovation Solution
A database system, referred to as the Fifth Medium (M5), that stores and manages declarative knowledge by representing concepts as unique identifiers and their relationships, allowing for the parsing and construction of complex mental concepts from text strings, enabling natural language communication and understanding through a proprietary tree structure and join table mechanism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current computer programs process text and perform word search and speech to text, then text processing capability is improved, but understanding of meaning in text deteriorates
Solution Approach 1:
The patent segments text processing into multiple stages: initial text processing (word search, speech-to-text) followed by a separate meaning interpretation stage using a semantic parser. This allows the system to maintain high text processing productivity while adding a dedicated component for meaning understanding that doesn't interfere with the speed of initial processing.
Solution Approach 2:
The patent introduces a semantic parser as an intermediary component between text processing and meaning understanding. This mediator translates processed text into a semantic representation that preserves meaning information, allowing the system to benefit from efficient text processing while preventing loss of meaning through the intermediary's interpretation layer.
2Adaptability or versatility
If a database system stores and manages declarative knowledge with complex relationships, then natural language understanding is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal semantic parser that handles multiple types of natural language constructs (deixis, metaphors, ambiguity, polysemy) through a single integrated system. This multi-functional approach improves natural language understanding versatility while avoiding the need for separate specialized systems for each linguistic phenomenon, thereby controlling overall system complexity.
Solution Approach 2:
The patent employs a nested database structure where concepts are organized in hierarchical levels with unique identifiers and foreign key relationships. This nesting allows complex declarative knowledge to be stored in a structured manner where simpler concepts form the foundation for more complex ones, enabling natural language understanding without requiring a flat, equally complex storage system.
3Measurement precision
If a parser constructs complex mental concepts from text strings, then concept interpretation accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-defining concept schemas and relationship structures in the database before text processing occurs. The semantic parser uses these pre-established frameworks to quickly map text strings to concepts, improving interpretation accuracy without requiring complex real-time analysis, thereby reducing processing time.
Solution Approach 2:
The patent uses copying by creating standardized concept representations with unique identifiers that can be reused across multiple text processing instances. Once a concept is parsed and identified, its representation can be copied and referenced elsewhere, improving accuracy through consistent interpretation while significantly reducing processing time by avoiding redundant parsing operations.
Data Source
AI summary
A system for natural language communication with a computer, comprising a database configures to store at least one table the table configured to store associations between concepts in a text string and associated concept identifiers; and a builder interfaced with the database, the builder configured to construct a concept defined by a text string by performing a process comprising the following steps: assigning a unique identifier to the concept, breaking the concept into sub-concepts, assigning a unique identifier to each sub-concept, using legal joins to join cub-concepts into higher order sub-concepts, assigning a unique identifier to the higher-order concepts, and repeating the steps until the higher order sub-concepts join to form the concept, and storing the concept, sub-concepts, higher order sub-concepts and unique identifiers in a table in the database.