Interpretation Graph for Natural Language Ambiguity
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Solution Overview
Problem
Current natural language processing systems are inefficient, inaccurate, and unreliable due to early pruning of alternative interpretations, which reduces their ability to understand the full range of human language richness and context.
Innovation Solution
The system creates an interpretation graph that represents all known ambiguous interpretations of a natural language input, using a series of processors to augment and manipulate this graph, compute confidence scores, and select the most likely interpretations, thereby preserving all possible meanings throughout the processing stages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If alternative interpretations are pruned at every step in natural language processing, then processing efficiency is improved, but accuracy and completeness of understanding deteriorates
Solution Approach 1:
The system performs preliminary actions by generating all possible interpretations of ambiguous words and phrases at the beginning of processing, storing them in an interpretation data structure with associated confidence scores, rather than making early decisions that would prune alternatives
Solution Approach 2:
The system dynamically adjusts the interpretation space by initially maintaining all possible interpretations with full detail, then progressively refining and filtering interpretations based on confidence scores and contextual analysis as processing continues, allowing flexibility at different stages
2Measurement precision
If all alternative interpretations are preserved throughout processing stages, then accuracy and completeness of understanding is improved, but computational complexity increases
Solution Approach 1:
The system applies partial action by maintaining full interpretation alternatives only for ambiguous elements while using simplified processing for unambiguous parts, and by using confidence score thresholds to selectively expand or prune interpretation branches based on their likelihood
Solution Approach 2:
The system changes parameters by representing interpretations as data structures with configurable confidence score thresholds, allowing dynamic adjustment of the balance between maintaining alternative interpretations and reducing computational load based on specific processing requirements
3Productivity
If simplified processing is applied (such as lower-casing all words), then processing speed is improved, but loss of information about alternative meanings occurs
Solution Approach 1:
The system performs preliminary action by capturing and preserving alternative interpretations (including case variations, word form variations, and contextual meanings) in structured data structures before any simplification processing occurs, ensuring information is retained for later analysis
Solution Approach 2:
The system creates copies of the interpretation data structures at different processing stages, allowing simplified processing to operate on copies while the original full-detail interpretations are preserved for accuracy-critical operations
Data Source
AI summary
Examples for efficiently representing, processing and deciding amongst multiple ambiguous interpretations of human natural language text are described. Processing includes creating and augmenting an “interpretation graph” which represents all known ambiguous interpretations of some natural language text. The interpretation graph is made of vertices (junction points which lead to alternative interpretations) and ‘lexical items’ (natural language objects representing data blocks, tokens, word parts, phrases, clauses, parts of speech, entities, or semantic interpretations) that represent alternative ambiguous interpretations of portions of the text. The examples show a set of simple operations for augmenting the interpretation graph to create alternative interpretations. Finally, the method includes a notion of “confidence”, which is computed as the graph is being constructed and can be used by a selector once the graph is complete to choose the most likely interpretation followed by any number of increasingly less likely interpretations. By saving all known ambiguous or alternative interpretations in an interpretation graph, the example system can provide better accuracy, reliability and coverage since possible alternatives are not pruned until the final end-to-end interpretation is selected.