Coupled Syntax Semantics Detection for NLP
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Solution Overview
Problem
Existing natural language processing techniques focus primarily on semantic embedding, neglecting the structural aspects of syntax, which are crucial for understanding and generating meaningful language, and fail to account for syntactic structures that can make physically distant words semantically closer.
Innovation Solution
A system and method that integrates syntax and semantics by generating semantic word and clause representations from syntactically-labeled contexts and syntax from common semantic relations between sequential words and clauses, using iterative processes to derive self-consistent notions of syntax and semantics, and applying these to natural language understanding and generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If semantic embedding techniques are used to represent word relationships, then semantic understanding is improved, but syntactic structure information is lost
Solution Approach 1:
The patent combines semantic embeddings with syntactic parse tree information into a unified representation. Semantic vectors are integrated with syntactic structural data, allowing the system to simultaneously capture both word meaning relationships and grammatical structure, thereby resolving the contradiction between semantic understanding and syntactic information preservation
Solution Approach 2:
The invention creates a composite representation that merges semantic vector spaces with syntactic tree structures. This composite approach integrates two different types of linguistic information (semantic and syntactic) into a single framework, enabling the system to utilize both semantic relationships and structural patterns without losing either component
2Productivity
If traditional semantic embedding methods are used, then processing speed is maintained, but long-range dependencies between words are missed
Solution Approach 1:
The patent adds a structural dimension to the traditional semantic embedding approach by incorporating parse tree information. This dimensional extension allows the system to capture long-range dependencies that exist in the syntactic structure while maintaining the efficiency of vector-based semantic representations, thereby preserving both processing speed and long-range relationship information
3Device complexity
If syntax and semantics are processed separately, then computational complexity is reduced, but natural language understanding accuracy deteriorates
Solution Approach 1:
The patent merges separate syntax and semantics processing into a unified coupled detection framework. By integrating syntactic parse information with semantic embeddings in a single computational process, the system achieves more accurate natural language understanding while avoiding the inefficiencies of completely separate processing pipelines
Data Source
AI summary
A method includes performing, with at least one processing device, natural language understanding using both (i) a semantic word and clause representation generated from syntactically-labeled context and (ii) a syntax generated from common semantic relations between sequential words and clauses. The semantic word and clause representation and the syntax could be generated iteratively. The method could include generating the semantic word and clause representation, where a significance of particular items of context is modified by a presence of words or clauses from a given lexicon of amplifiers along a syntax tree. The method could also include generating the syntax, where multiple semantic relations are considered to be equivalent if the multiple semantic relations have identical representations in an auto-associative memory. The semantic word and clause representation could include content semantics constructed from closest nodes in a syntax tree to a clause.