Semantic Trigger Promotion via Parse Tree Context Analysis
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Solution Overview
Problem
Current natural language processing techniques, such as bag-of-words models and bi-gram/tri-gram models, fail to effectively capture the semantic meaning of text due to their inability to preserve word order and structure, especially in long sentences with multiple changes in semantic polarity, leading to limited insight into contextual meanings.
Innovation Solution
The method involves generating a parse tree for text to identify contextual and tentative triggers, where tentative triggers are promoted to contextual trigger types based on local contextual analysis, using modules like parse tree generation, trigger identification, conflict resolution, and contextual promotion to resolve ambiguous semantic interpretations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If bag-of-words model is used, then processing is simple, but semantic meaning cannot be captured due to loss of word order and structure information
Solution Approach 1:
The patent segments the text into sentences and further into phrases, analyzing each segment's semantic properties independently before integrating them into an overall semantic representation. This allows preservation of word order and structural information while maintaining processing manageability through modular analysis units.
Solution Approach 2:
The patent transitions from flat bag-of-words representation to a multi-dimensional semantic space where sentences are represented as sequences of semantic phrases with associated metadata about their structure, position, and semantic relationships. This dimensional expansion preserves word order and structural information while enabling sophisticated semantic analysis.
2Loss of information
If bi-gram or tri-gram models are used, then some semantic context is captured, but performance deteriorates in long sentences with multiple changes of semantic polarity
Solution Approach 1:
The patent divides long sentences into smaller semantic phrases and clauses, analyzing each segment's semantic polarity and context independently. This segmentation prevents the accumulation of semantic confusion in long sentences with multiple polarity changes, as each segment is processed and interpreted locally before integration.
Solution Approach 2:
The patent implements iterative semantic analysis where the semantic interpretation of each phrase is validated against the overall sentence context and previous interpretations. This feedback mechanism allows the system to detect and correct semantic polarity changes, maintaining accurate interpretation even in complex sentences with multiple semantic shifts.
3Measurement precision
If parse tree generation is used to identify triggers, then semantic analysis accuracy is improved, but complexity of the system increases
Solution Approach 1:
The patent segments the complex parse tree generation process into modular components: sentence segmentation, phrase identification, trigger detection, and semantic interpretation. Each module handles a specific aspect of the analysis independently, making the overall complex system more manageable and easier to implement while maintaining high accuracy through coordinated operation of these modular components.
Data Source
AI summary
Methods, systems and computer readable media are provided for semantic evaluation of tentative triggers based on contextual triggers. Contextual triggers are identified within text. A parse tree comprising a plurality of nodes is generated corresponding to the text. Tentative triggers are identified within the text. A determination is made as to whether one or more nodes of the parse tree corresponding to the tentative trigger is within a context of one or more nodes of the parse tree corresponding to the contextual triggers. Based on the determination, the tentative trigger type is assigned to a contextual trigger type.