Query Canonicalization Models for Search Intent Accuracy
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Solution Overview
Problem
Existing search systems face inefficiencies in processing natural language search queries, particularly in determining well-formedness and generating well-formed variants, which affects query understanding and resource utilization.
Innovation Solution
The use of trained classification and canonicalization models, such as neural network models, to determine the well-formedness of search queries and generate well-formed variants, conserving resources by selective processing and improving user query intent understanding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a trained classification model is used to determine well-formedness of search queries, then query understanding accuracy is improved, but computational overhead increases
Solution Approach 1:
The classification model is trained in advance on a large dataset of search queries to learn patterns of well-formed and malformed queries. This preliminary training allows the model to make rapid accuracy judgments during actual search operations without performing complex real-time analysis, thus improving query understanding while keeping operational computational overhead manageable.
Solution Approach 2:
Instead of performing complex linguistic analysis for every query, the system creates a simplified copy or representation of query structure through the trained model. The model captures essential grammatical and semantic patterns during training, then uses these learned patterns to quickly assess new queries without reproducing the full complexity of linguistic analysis each time.
2Manufacturing precision
If a trained canonicalization model is used to generate well-formed query variants, then search result quality is improved, but processing time increases
Solution Approach 1:
The canonicalization model is pre-trained on extensive corpora of search queries and their corrected variants. During actual search processing, the model leverages this pre-learned knowledge to generate well-formed query variants rapidly, avoiding time-consuming real-time linguistic analysis while maintaining high search result quality.
Solution Approach 2:
The system replaces traditional rule-based query rewriting mechanisms with a neural network-based canonicalization model. This substitution allows the system to learn complex query transformation patterns from data rather than relying on manually crafted rules, improving both the quality of generated variants and the efficiency of processing.
3Productivity
If selective processing using classification models is implemented, then resource utilization is improved, but system complexity increases
Solution Approach 1:
The query processing system is divided into distinct segments: a classification model that quickly assesses query well-formedness, and a canonicalization model that generates variants only when needed. This segmentation allows the system to apply computational resources selectively - using the lightweight classification model for all queries while reserving the more resource-intensive canonicalization model only for malformed queries, thus improving overall resource utilization.
Solution Approach 2:
The classification model serves as an intermediary between the user's input query and the canonicalization model. It acts as a gatekeeper that filters queries, directing only those that are malformed to the canonicalization model. This intermediary layer enables selective processing that improves resource utilization while managing system complexity through a clear hierarchical structure.
Data Source
AI summary
Techniques are described herein for training and/or utilizing a query canonicalization system. In various implementations, a query canonicalization system can include a classification model and a canonicalization model. A classification model can be used to determine if a search query is well-formed. Additionally or alternatively, a canonicalization model can be used to determine a well-formed variant of a search query in response to determining a search query is not well-formed. In various implementations, a canonicalization model portion of a query canonicalization system can be a sequence to sequence model.