Joint Syntactic Semantic Disambiguation Framework
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Solution Overview
Problem
Current methods fail to robustly disambiguate syntactic ambiguities in natural language, such as prepositional phrase attachments, which is essential for deep semantic interpretation and applications like machine translation and natural language control of robots.
Innovation Solution
A unified framework for resolving both semantic and syntactic ambiguities through a multi-context disambiguator that generates and selects the best combinations of semantic interpretation choices for alternative syntactic parses, using a computer-readable medium and apparatus with enumerators, combinators, and selectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate methods are used for syntactic and semantic disambiguation, then each can be processed independently, but the overall disambiguation accuracy is insufficient
Solution Approach 1:
The patent combines syntactic disambiguation and semantic disambiguation into a unified framework where both processes operate simultaneously on the same data structures. The syntactic parser generates multiple parse trees, and the semantic analyzer evaluates each tree's meaning, allowing the system to select the best combination of syntactic and semantic interpretations rather than processing them separately in sequence.
Solution Approach 2:
The disambiguation system is designed to handle multiple types of ambiguities (syntactic, semantic, and their interactions) through a single integrated framework. The same computational infrastructure processes both syntactic parse alternatives and semantic interpretation alternatives, making the system versatile for different disambiguation tasks without requiring separate specialized systems.
2Reliability
If all alternative parses are fully analyzed, then disambiguation completeness is maximized, but computational resources are excessively consumed
Solution Approach 1:
The system performs partial analysis of alternative parses by evaluating them to different depths based on their likelihood. High-probability parses receive full semantic analysis while low-probability parses are pruned early, avoiding unnecessary computational expenditure on unlikely interpretations while still maintaining completeness for plausible alternatives.
Solution Approach 2:
The semantic evaluation results feed back into the syntactic disambiguation process, allowing the system to adjust which syntactic alternatives warrant further analysis. When semantic analysis reveals that certain syntactic interpretations lead to semantically implausible meanings, this feedback prunes those branches, reducing computational resources needed while maintaining accuracy.
Data Source
AI summary
Ambiguities in a natural language expression are interpreted by jointly disambiguating multiple alternative syntactic and semantic interpretations. More than one syntactic alternative, represented by parse contexts, are analyzed together with joint analysis of referents, word senses, relation types, and layout of a semantic representation for each syntactic alternative. Best combinations of interpretations are selected from all participating parse contexts, and are used to form parse contexts for the next step in parsing.