NLP System Contextualizing Words to Knowledge Base
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Solution Overview
Problem
Conventional natural language processing (NLP) systems face challenges in understanding freely-formed natural language messages due to their reliance on template-based approaches, which are inflexible and unable to handle the arbitrary recursive compositionality of language, limiting their ability to generate appropriate responses in interactive conversations.
Innovation Solution
The development of an improved NLP system that employs compositional parsing techniques, named entity recognition, syntactic parsing, and reduction rules to extract meaning from natural language inputs, integrating with a knowledge base to understand user intent and generate responses, while also learning and adapting to conversational inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If template-based approaches are used for NLP, then system design is simplified with predefined patterns, but the system cannot handle arbitrary recursive compositionality of language and freely-formed natural language messages
Solution Approach 1:
The patent segments natural language processing into distinct components: named entity recognition, syntactic parsing, and semantic interpretation. This segmentation allows the system to handle complex language structures by breaking them down into manageable parts that can be processed independently and then integrated, resolving the contradiction between structured design and handling free-formed language.
Solution Approach 2:
The patent implements dynamic parsing that adapts to the structure of input messages rather than relying on fixed templates. The syntactic parser dynamically builds grammatical hierarchies based on the actual message structure, and the named entity recognition dynamically identifies entities based on context, enabling the system to handle arbitrary language composition while maintaining a systematic approach.
2Device complexity
If conventional flat intent parsers are used, then implementation is straightforward with pattern matching, but the system cannot extract meaning from arbitrary recursive compositionality of language
Solution Approach 1:
The patent transitions from flat pattern matching to hierarchical syntactic parsing, adding a dimensional structure to the parsing process. By organizing parsing into multiple levels (tokenization, named entity recognition, syntactic hierarchy, semantic interpretation), the system achieves both systematic implementation and precise meaning extraction from complex language structures.
Solution Approach 2:
The patent implements nested parsing structures where syntactic phrases are nested within grammatical hierarchies, which are nested within semantic interpretations. This nested architecture allows the system to maintain clear implementation boundaries at each level while capturing the recursive compositionality of language, resolving the contradiction between implementation simplicity and meaning extraction accuracy.
3Adaptability or versatility
If the NLP system uses a separate knowledge base from the NLG system, then each system can be independently optimized, but the systems become misaligned and the NLG system cannot understand NLP inferences
Solution Approach 1:
The patent merges the knowledge bases of the NLP and NLG systems by having the NLP system leverage the same knowledge base that supports the NLG system. This integration ensures that both systems operate from a common understanding of the domain, allowing the NLG system to generate responses that are directly aligned with the NLP system's inferences while still allowing independent optimization of each system's processing logic.
Data Source
AI summary
Disclosed herein is an NLP system that is able to extract meaning from a natural language message using improved parsing techniques. Such an NLP system can be used in concert with an NLG system to interactively interpret messages and generate response messages in an interactive conversational stream. The parsing can include (1) named entity recognition that contextualizes the meanings of words in a message with reference to a knowledge base of named entities understood by the NLP and NLG systems, (2) syntactically parsing the message to determine a grammatical hierarchy for the named entities within the message, (3) reduction of recognized named entities into aggregations of named entities using the determined grammatical hierarchy and reduction rules to further clarify the message's meaning, and (4) mapping the reduced aggregation of named entities to an intent or meaning, wherein this intent/meaning can be used as control instructions for an NLG process.