Token-wise Entity Classification for Semantic Table Representations
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Solution Overview
Problem
Existing natural language processing solutions face inefficiencies and reliability issues in processing table data objects, particularly in improving predictive accuracy and training speed without sacrificing accuracy or speed.
Innovation Solution
The method involves generating semantic table representations using a token-wise entity type classification mechanism with an inter-related entity type taxonomy, creating a graph-based table representation, and performing prediction-based actions, which enhances predictive accuracy and training efficiency by reducing computational operations and data entries needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing natural language processing solutions are used for processing table data objects, then basic processing functionality is provided, but predictive accuracy and training speed are insufficient
Solution Approach 1:
The patent segments the table data processing into multiple stages: initial table data reception, graph-based table representation generation with node creation for each table cell, entity type classification for each node, and semantic table representation generation. This segmentation allows the system to process table data in manageable units while maintaining high predictive accuracy through detailed entity classification at each stage.
Solution Approach 2:
The patent performs preliminary actions by pre-defining entity type taxonomies and relationships before processing table data. The system pre-establishes the graph structure framework and entity type classifications, which accelerates training speed by avoiding runtime computation of these fundamental structures while maintaining predictive accuracy through pre-validated entity relationships.
2Measurement precision
If computational operations and data entries are increased to improve predictive accuracy, then prediction quality improves, but training speed and resource usage efficiency deteriorate
Solution Approach 1:
The patent extracts only the essential entity types and relationships needed for accurate prediction from the complete table data. The graph-based representation extracts key semantic relationships between table cells, and the entity type classification extracts only the necessary classification information. This extraction maintains predictive accuracy by focusing on critical features while reducing training time by eliminating redundant computational operations.
Solution Approach 2:
The patent changes parameters by transforming raw table data into structured graph representations with defined entity types and relationships. This parameter transformation organizes data in a way that improves predictive accuracy through structured semantic relationships while reducing training time by creating a more efficient data structure for model processing.
3Loss of information
If a comprehensive entity type classification system is implemented, then semantic understanding improves, but system complexity increases
Solution Approach 1:
The patent adds a new dimension to table data processing by creating a graph-based representation layer above the raw table structure. This dimensional transformation organizes semantic information in a hierarchical graph structure with nodes and relationships, improving semantic understanding while managing complexity through structured organization of entity types and their relationships.
Solution Approach 2:
The patent introduces an intermediary entity type classification layer between raw table data and the final semantic representation. This intermediary layer, consisting of predefined entity type taxonomies and relationship definitions, bridges the gap between raw data and semantic understanding, improving information retention while controlling system complexity through standardized classification categories.
Data Source
AI summary
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing natural language processing operations by generating semantic table representations for table data objects using a token-wise entity type classification mechanism whose output space is defined by a set of defined entity types characterized by an inter-related entity type taxonomy to generate a representation of a table data object that describes per-token semantic inferences and cross-token semantic inferences performed on the table data object in accordance with subject-matter-domain insights as described by the inter-related entity type taxonomy.