Synonym Identification Using Non-Adjacent Query Context
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Solution Overview
Problem
Current search engines struggle to accurately identify synonyms for query terms by relying solely on adjacent context, which can lead to suboptimal search query revisions and reduced search result quality.
Innovation Solution
The method evaluates candidate synonyms using both adjacent and non-adjacent contexts within a search query, assigning confidence values based on the presence of additional terms in the query to determine the relevance of synonyms, thereby improving the accuracy of synonym identification and search query revisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If synonym evaluation relies solely on adjacent context terms, then the evaluation process is simple and fast, but the accuracy of synonym identification deteriorates
Solution Approach 1:
The context is segmented into adjacent terms (immediate neighbors) and non-adjacent terms (other terms in the query). The system evaluates synonyms using both segments, with non-adjacent terms providing additional contextual signals. This segmentation allows the system to incorporate more information without overwhelming the evaluation process, thereby improving synonym identification accuracy while maintaining manageable complexity.
Solution Approach 2:
The system transitions from one-dimensional adjacent context evaluation to two-dimensional context evaluation by incorporating non-adjacent terms. This adds a new dimension (distance from the query term) to the context analysis, enabling more comprehensive synonym evaluation that captures both local and global query context, thus improving accuracy without excessive complexity increase.
2Productivity
If only adjacent terms are used to evaluate synonyms, then the processing speed is high, but the search result quality deteriorates
Solution Approach 1:
The system performs partial context evaluation by focusing on key non-adjacent terms rather than all possible terms. It selectively incorporates non-adjacent context terms that provide meaningful signals for synonym evaluation, avoiding the need to process every term in the query. This partial action approach maintains processing speed while improving search result quality through enhanced synonym identification.
3Measurement precision
If non-adjacent context terms are incorporated into synonym evaluation, then the confidence in synonym selection improves, but the computational resources required increase
Solution Approach 1:
The system performs preliminary filtering of non-adjacent context terms before full synonym evaluation. It pre-identifies which non-adjacent terms are relevant to the query term being evaluated, avoiding unnecessary computational processing of irrelevant terms. This preliminary action reduces computational energy consumption while maintaining high synonym confidence scores by focusing resources on meaningful context terms.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identifying a particular query term of an original search query, identifying a candidate synonym for the particular query term in context with an other non-adjacent query term of the original search query that is not adjacent to the particular query term in the original search query, accessing stored data that specifies, for a pair of terms that includes the particular query term and the candidate synonym of the particular query term, a respective confidence value for the other non-adjacent query term, determining that, in the stored data, the confidence value for the other non-adjacent query term satisfies a threshold, and determining to revise the original search query to include the candidate synonym of the particular query term, based on determining that the confidence value the other non-adjacent query term satisfies the threshold.