NLP Parse Tree Traversal for Entity-Temporal Relation Inference
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Solution Overview
Problem
Current natural language processing systems face ambiguities in associating temporal elements with entity elements, such as dates with medical procedures or medications, due to the limitations of existing ontologies and representation methods.
Innovation Solution
A computer-implemented method that organizes text as a natural language processing (NLP) parse tree, traverses the tree to concatenate nodes, and generates relation types between entity and temporal elements, thereby clarifying their associations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing ontologies are used to represent knowledge relationships, then the system can manage semantic concepts, but ambiguities remain in associating temporal elements with entity elements
Solution Approach 1:
The patent segments the text processing into distinct phases: parsing the text into a parse tree, identifying temporal and entity elements, traversing the parse tree to find relationships, and generating relation types. This segmentation allows each phase to focus on specific aspects of the problem, improving overall accuracy in resolving temporal associations.
Solution Approach 2:
The parse tree serves as an intermediary structure between the raw text and the final relation type generation. It organizes the text into hierarchical nodes that facilitate systematic traversal and relationship identification, mediating between the ambiguity of natural language and the precision required for accurate temporal association.
2Adaptability or versatility
If ontologies are designed to be comprehensive, then more knowledge can be represented, but they become more complex and harder to maintain
Solution Approach 1:
The patent creates a universal approach that works across multiple domains (medical, legal, financial) by using a general parse tree traversal methodology. The same core algorithm can identify temporal relationships in different contexts without requiring domain-specific complex ontology structures, achieving versatility through a unified approach.
Solution Approach 2:
The system uses the text itself to generate the relationship information through parse tree traversal, rather than relying on pre-defined complex ontology entries. The text's own structure provides the guidance needed for relationship identification, making the system self-sufficient and reducing dependency on complex external knowledge bases.
3Measurement precision
If the system processes text to generate relation types, then association accuracy improves, but processing time increases
Solution Approach 1:
The patent performs preliminary parsing of the text into a parse tree structure before attempting to identify temporal relationships. This preliminary organization of the text data facilitates faster and more accurate relationship detection during the subsequent traversal phase, as the structured format eliminates the need for re-processing the entire text.
Solution Approach 2:
The system extracts and identifies only the relevant temporal elements and entity elements from the text, focusing the relationship detection algorithm only on these extracted components rather than processing the entire text uniformly. This extraction approach reduces processing time while maintaining high accuracy for the critical relationship identification task.
Data Source
AI summary
Examples described herein provide a computer-implemented method that includes receiving, by a processing device, the span of text, the span of text comprising a plurality of elements including at least an entity element and a temporal element. The method further includes organizing, by the processing device, the span of text as a natural language processing (NLP) parse tree. The method further includes traversing, by the processing device, the NLP parse tree by concatenating individual nodes of the span of text to generate the relation type between the entity element and the temporal element. The method further includes associating, by the processing device, the entity element, the relation type, and the temporal element together.