Semantic Content Inversion for Search Relevance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current natural language search engines rely on keyword matching, often returning irrelevant results and failing to utilize semantic understanding of phrases and sentences, leading to users having to sift through numerous reviews and opinions that may not align with their specific interests or needs.
Innovation Solution
A content processing and search system that inverts semantic structures into syntactic information, analyzing sentiment and user needs expressions by forming syntactic parse trees, allowing for the grouping of similar sentiments and features to provide targeted search results and product recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If keyword-based search is used, then search simplicity is maintained, but search result relevance deteriorates
Solution Approach 1:
The patent replaces traditional keyword-matching mechanical search with a semantic understanding system that uses syntactic parse trees and sentiment analysis. This substitution enables the system to comprehend the meaning and intent behind search queries rather than simply matching keywords, thereby improving result relevance while maintaining user-friendly operation.
Solution Approach 2:
The patent transforms the search parameter from simple keyword presence to complex semantic features extracted through syntactic analysis. By changing the search parameters to include sentiment polarity, subject objects, and feature attributes derived from parse trees, the system achieves more relevant results without compromising ease of use.
2Quantity of substance
If all user reviews are presented, then comprehensive information is provided, but information filtering efficiency deteriorates
Solution Approach 1:
The patent extracts specific sentiment expressions and feature information from user reviews using syntactic parse trees. By taking out only the relevant sentiment polarity, subject, and feature attributes, the system presents filtered, targeted information that maintains completeness of useful data while eliminating irrelevant content, thus reducing user filtering time.
Solution Approach 2:
The patent segments user reviews into distinct sentiment expressions and feature mentions through parse tree analysis. This segmentation allows the system to organize and present review information by relevant features and sentiment categories, enabling users to quickly find specific information without wading through entire review texts.
3Measurement precision
If semantic analysis is implemented, then search accuracy is improved, but system complexity increases
Solution Approach 1:
The patent introduces syntactic parse trees as an intermediary structure between raw text input and semantic interpretation. This intermediary enables systematic extraction of sentiment expressions and features through standardized grammatical analysis, improving search accuracy while managing complexity through a structured intermediate representation rather than direct complex NLP processing.
4Measurement precision
If sentiment-based filtering is applied, then result relevance is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary syntactic analysis and sentiment extraction on user reviews during the indexing phase, storing parsed sentiment information and feature attributes in advance. When a search is executed, the system queries this pre-processed data directly, achieving fast sentiment-based filtering and relevant results without time-consuming real-time analysis.
Data Source
AI summary
There is provided systems and method for content inversion for use in user searches and product recommendations. The methods include receiving a first content, wherein the first content includes a first sentence having at least one first sentiment expression, creating a syntactic parse tree of the first sentence, identifying a first sentiment in the at least one first sentiment expression, wherein the first sentiment corresponds to a first polarity, determining a first needs expression corresponding to the first sentiment, wherein the first needs expression includes a first subject. The method may further include creating a sub-tree corresponding to the first needs expression, and grouping the content with other content having similar sub-trees.