Logical Parser for NLP Knowledge Propagation

The patent segments the NLP processing into distinct modules: a parser that converts natural language to a hierarchical representation, a knowledge propagation module that distributes relevance values, and a reasoning module that performs logical operations. This segmentation allows each component to specialize in processing logical relationships accurately without overwhelming the entire system, thereby improving accuracy while managing complexity through modular architecture.

The patent introduces an intermediary hierarchical representation structure that mediates between raw natural language input and final reasoning outputs. This intermediate form organizes information into nodes with relevance values, enabling accurate logical relationship processing while isolating the complexity of reasoning operations from the basic language processing pipeline.

The patent performs preliminary action by pre-establishing the hierarchical representation structure and pre-computing relevance values during the parsing phase. This preparation occurs before actual reasoning operations, so that when reasoning is needed, the system can operate more efficiently on pre-organized data rather than processing raw language continuously, thereby reducing reasoning time while maintaining accuracy.

The knowledge propagation mechanism operates continuously during parsing to distribute relevance values throughout the hierarchical representation. This continuous propagation ensures that the structure is always in a ready state for reasoning operations, eliminating the need for separate propagation steps and reducing overall processing time while maintaining high reasoning accuracy.