Resolving Temporal Ambiguities in Natural Language via Syntax Tree Permutations
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Solution Overview
Problem
Natural language processing systems struggle to resolve temporal ambiguities in digital assistant interactions, such as interpreting phrases like 'this week or next' due to the lack of explicit temporal references, leading to inefficiencies in scheduling meetings.
Innovation Solution
The system employs artificial intelligence to parse and resolve ambiguous temporal components by generating and ranking syntax tree permutations using trained machine learning models, identifying the most likely temporal intent from natural language inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If natural language processing is used to interpret temporal expressions, then user convenience is improved, but ambiguity resolution accuracy deteriorates
Solution Approach 1:
The patent segments the temporal expression processing into multiple components: identifying temporal expressions, generating syntax tree permutations, scoring each permutation, and selecting the highest-scoring interpretation. This segmentation allows the system to handle ambiguity systematically by breaking down the complex interpretation task into manageable steps, thereby improving accuracy while maintaining ease of use.
Solution Approach 2:
The patent introduces syntax trees as an intermediary representation between the raw natural language input and the final temporal interpretation. These syntax trees serve as a structured intermediate form that captures different possible interpretations of ambiguous temporal expressions, allowing the system to evaluate and resolve ambiguities systematically before producing the final result.
2Measurement precision
If multiple syntax tree permutations are generated to resolve ambiguities, then interpretation accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent generates multiple syntax tree permutations (excessive action) to ensure all possible interpretations are considered, but then applies a scoring mechanism to filter and rank these permutations. This approach allows the system to explore multiple possibilities thoroughly while managing complexity by systematically evaluating and selecting only the most relevant interpretations based on learned patterns.
Solution Approach 2:
The patent changes the parameter of representation from raw natural language to structured syntax trees with associated confidence scores. This parameter transformation allows the system to work with a more manageable and structured form that facilitates systematic evaluation and comparison of different interpretations, thereby reducing processing complexity while maintaining accuracy.
3Productivity
If implicit temporal references are resolved automatically, then scheduling efficiency is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary action by pre-training machine learning models on large corpora of temporal expressions and their correct interpretations. This preliminary training enables the system to quickly resolve ambiguities during actual scheduling operations, as the models have already learned the patterns and relationships. The heavy computational work is done in advance, allowing fast real-time processing.
Solution Approach 2:
The patent replaces manual clarification processes (mechanical interaction between user and system) with automated machine learning-based resolution. Instead of requiring users to explicitly clarify ambiguous temporal references through back-and-forth communication, the system automatically resolves these ambiguities using trained models, significantly reducing processing time and improving scheduling efficiency.
Data Source
AI summary
In non-limiting examples of the present disclosure, systems, methods and devices for resolving temporal ambiguities are presented. A natural language input may be received. A temporal component of the input may be identified. A determination may be made that the temporal component includes a conjunction that separates temporal meeting block alternatives. A temporal ambiguity may be identified in one of the meeting block alternatives. A plurality of syntax tree permutations may be generated for the meeting block alternative where the ambiguity was identified. A machine learning model that has been trained to identify a most relevant permutation for a given natural language input may be applied to each of the plurality of permutations. A temporal meeting block alternative corresponding to the most relevant permutation may be surfaced.