Natural Language Processing Matrices for Token Dependency
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Solution Overview
Problem
Current natural language processing systems face challenges in accurately analyzing and structuring unstructured natural language data, particularly in large corpora, which affects the reliability and accuracy of diagnosis systems and other applications, as they struggle to effectively represent and manipulate linguistic relationships within phrases.
Innovation Solution
A method is introduced to generate a matrix structure for natural language phrases, using a parse tree data structure to represent dependencies between tokens, allowing for a clear and readable format that indicates the order and levels of tokens, enabling easier human verification and modification while maintaining data fidelity for computational operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a parse tree data structure is used to represent dependencies between tokens, then the linguistic relationships are accurately represented, but the format becomes complex and difficult to read and verify
Solution Approach 1:
The patent segments the parse tree into a matrix format where tokens are arranged in rows and columns based on their dependency levels and positions. This segmentation transforms the hierarchical nested structure of parse trees into a flattened tabular representation, making it easier to read and verify while preserving the linguistic relationship information.
Solution Approach 2:
The patent introduces an intermediary matrix structure that mediates between the complex parse tree representation and simple text formats. This matrix acts as a bridge, organizing token dependency information in a structured way that is both computationally useful and human-readable, allowing verification without losing accuracy.
2Device complexity
If unstructured natural language data is processed directly, then the system is simpler to implement, but the reliability and accuracy of analysis is reduced
Solution Approach 1:
The patent applies preliminary action by converting unstructured natural language data into a structured matrix format before further processing. This preliminary structuring step organizes the data into rows and columns representing tokens and their dependencies, making subsequent analysis more reliable and accurate while maintaining implementation simplicity.
3Ease of operation
If a matrix structure is generated from parse tree data, then human verification and modification becomes easier, but the computational manipulation capability is reduced
Solution Approach 1:
The patent makes the matrix structure universal by designing it to serve multiple functions: it facilitates human verification and modification through its readable format, while simultaneously enabling computational manipulation through its structured data organization. The same matrix structure supports both human and machine processing operations.
Data Source
AI summary
A phrase may be received that includes a plurality of tokens in a natural language format. A plurality of levels relating to dependencies between tokens of the plurality of tokens within the phrase is determined. A matrix structure is generated for the phrase. The matrix structure utilizes a plurality of rows and a plurality of columns to store data of the phrase. The plurality of rows and the plurality of columns each indicate one of an order of tokens of the plurality of tokens or levels of the plurality of levels.