Vector-Based Synonym Generation for Search Relevance
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Solution Overview
Problem
Search engines face challenges in providing relevant results due to variations in word meanings, spellings, and contexts, which are not effectively addressed by exact matching techniques, leading to incongruities between search queries and relevant documents.
Innovation Solution
A system and method for automatically generating synonym sets by representing phrases as vector data, calculating similarity between vectors, and filtering to determine synonyms, utilizing data sources such as user interactions, text data, and logs to analyze contexts and user actions, thereby identifying phrases with shared meanings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If exact matching of words is used in search engines, then the simplicity and speed of search processing is maintained, but relevant documents are left out due to spelling variations, grammatical forms, and synonym differences
Solution Approach 1:
The system pre-computes and stores vector representations for all phrases in the document collection before search queries are processed. This preliminary action allows the search engine to perform efficient similarity comparisons without complex real-time processing, resolving the contradiction between comprehensive synonym matching and processing simplicity
Solution Approach 2:
The patent replaces traditional mechanical text matching mechanisms with vector space model and mathematical similarity calculations. Instead of comparing words directly, the system transforms text into numerical vectors and uses mathematical operations to determine semantic similarity, thereby improving relevance while maintaining computational efficiency
2Measurement precision
If a manual synonym knowledge database is built to account for context-dependent word meanings, then the accuracy of synonym recognition is improved, but the effort and time required to maintain and update the database continuously increases
Solution Approach 1:
The system automatically learns and updates synonym relationships by analyzing user interactions, search queries, and document contexts without requiring manual intervention. The vector representations are continuously refined based on observed usage patterns, enabling the system to self-update synonym knowledge while adapting to evolving language usage
Solution Approach 2:
The patent incorporates feedback loops where user search behavior, click-through rates, and interaction patterns are continuously analyzed to refine vector representations and synonym relationships. This feedback mechanism allows the system to automatically improve synonym recognition accuracy based on real-world usage data, eliminating the need for manual database maintenance
3Adaptability or versatility
If traditional search matching is used, then the processing speed is maintained, but the ability to adapt to evolving word meanings and contextual variations is lost
Solution Approach 1:
The patent implements dynamic vector representations that evolve over time based on usage patterns and contextual information. Instead of static synonym dictionaries, the system continuously updates vector embeddings to reflect changing language usage, new meanings, and emerging terminology, enabling adaptation to evolving language while using efficient approximate nearest neighbor search algorithms
Data Source
AI summary
A system for synonym generation and/or identification can be utilized to make a search engine more effective in finding relevant search results. In embodiments, a synonym generation system includes a phrase vector module, a vector similarity module, and a vector filter module. Candidate synonym phrase pairs are selected from data sources for analysis. Data sources may include a log of search queries, a corpus of web text, and a set of merchant descriptions of products. The data sources may be analyzed with respect to the phrase pairs to generate vector representations of the phrase pairs. The vector representations may then be analyzed to determine a similarity vector. The similarity vector allows the synonym generation system to filter synonyms from the candidate phrase pairs.