Clause-Based Semantic Parsing for Complex Statements
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Solution Overview
Problem
Traditional machine learning models for semantic parsing struggle with accurately parsing statements that have a complicated structure due to limitations in their training corpus, leading to difficulties in handling such inputs effectively.
Innovation Solution
A clause-based semantic parsing approach is employed, where a first clause with independent semantics is identified from a target statement, converted to a first intermediate statement based on a logical representation, and further logical representations are determined iteratively to generate a target logical representation, overcoming the limitations of traditional models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning models are used for semantic parsing, then parsing capability is provided, but accuracy deteriorates for complicated statement structures due to training corpus limitations
Solution Approach 1:
The patent segments the semantic parsing task into multiple stages by dividing the statement into clauses and processing them independently. Each clause is parsed separately into logical representations, which are then combined to form the complete semantic representation. This segmentation allows the model to handle complex statements by breaking them down into manageable units, improving overall parsing accuracy without requiring extensive training data for all possible statement structures.
2Measurement precision
If traditional grammar-based semantic parsing is used, then parsing accuracy is maintained, but adaptability to new languages and structures deteriorates
Solution Approach 1:
The patent introduces an intermediary layer of logical representations that bridges the gap between natural language clauses and final semantic meanings. Instead of directly mapping grammar rules to semantic representations, the system uses intermediate logical forms that can be systematically combined. This intermediary approach maintains the rigor of grammar-based parsing while enabling better adaptation to new languages and structures through the clause combination mechanism.
3Measurement precision
If clause-based semantic parsing is implemented, then parsing accuracy for complex statements is improved, but processing complexity increases
Solution Approach 1:
The parsing process is segmented into distinct phases: clause identification, logical representation generation for each clause, and combination of representations. This segmentation allows each phase to be optimized independently and enables parallel processing of clauses, reducing the overall computational complexity despite the increased number of processing steps.
Solution Approach 2:
The system performs preliminary actions by pre-identifying clauses and generating their logical representations before final combination. This preliminary processing can be done in a systematic, rule-based manner that reduces the complexity of the final combination step, as the individual clause representations are already prepared and standardized.
Data Source
AI summary
According to implementations of the subject matter described herein, a clause-based semantic parsing solution is provided. In the solution, a first clause with independent semantics is determined from a target statement. The target statement is converted to a first intermediate statement based on a first logical representation corresponding to the first clause. Subsequently, at least one logical representation corresponding to at least part of semantics of the first intermediate statement is determined. The first logical representation and the at least one logical representation may be used to determine a target logical representation corresponding to semantics of the target statement. Therefore, more accurate semantic parsing can be achieved.