Semantic Text Analysis via Grammatical Structure and Keyword Sets
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Solution Overview
Problem
Current Natural Language Processing (NLP) tools primarily focus on structured information and basic information retrieval, failing to effectively analyze the semantic content of textual phrases and determine semantic similarities, which is crucial for advanced applications like information retrieval and question answering.
Innovation Solution
A method that analyzes the grammatical structure and generates keyword sets for text phrases, identifies idiomatic expressions, and uses semantic similarity metrics to determine the similarity between phrases, enabling semantic analysis and retrieval of relevant documents or answers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If basic NLP tools are used for information retrieval, then processing speed is maintained, but semantic understanding capability is insufficient
Solution Approach 1:
The patent segments the text analysis process into distinct components: grammatical structure analysis, keyword extraction, and semantic similarity calculation. Each component processes specific aspects of the text independently, then combines results to achieve comprehensive semantic understanding without overwhelming system complexity
Solution Approach 2:
The patent adds a new dimension of analysis by incorporating grammatical structure parsing alongside traditional keyword matching. This multi-dimensional approach (structural + lexical) enables deeper semantic understanding while maintaining manageable complexity through modular processing
2Measurement precision
If semantic analysis is performed on all text phrases, then retrieval accuracy is improved, but processing time increases
Solution Approach 1:
The patent extracts only the essential elements needed for semantic comparison: key grammatical structures and significant keywords. By filtering out non-essential text components and focusing only on critical semantic carriers, the system achieves accurate retrieval without processing the entire text corpus
Solution Approach 2:
The patent applies partial semantic analysis by focusing computational resources on the most semantically significant portions of text (key phrases and structures) rather than performing exhaustive analysis on every word, achieving sufficient accuracy with reduced processing time
3Adaptability or versatility
If multiple text phrases with different expressions are analyzed, then coverage of semantic variations is improved, but difficulty in determining similarity increases
Solution Approach 1:
The patent transforms diverse text expressions into a standardized parameter set consisting of grammatical structure features and keyword vectors. By converting various linguistic expressions into common structural and lexical parameters, the system can compare semantically similar phrases regardless of their surface-level differences
Data Source
AI summary
A method of comparing the semantic similarity of two different text phrases in which the grammatical structure of the two different text phrases is analyzed and a keyword set for each of the different text phrases is derived The semantic similarity of the phrases can be determined in accordance with the grammatical structure of the two different text phrases and the contents of the two keyword sets.