SMT-Based Query Spelling Correction for Dynamic Web Search
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Solution Overview
Problem
Traditional dictionary-based techniques for search query spelling correction are inadequate for web searches due to the dynamic nature of the web, as they struggle to capture new phrases, names, abbreviations, and informal terms, leading to incomplete and unsatisfying results.
Innovation Solution
Statistical Machine Translation (SMT) based search query spelling correction techniques are employed, where search data is logged to ascertain error patterns and develop query correction models that translate misspelled substrings to corrected substrings, combining probabilistic features from multiple models to rank correction candidates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If dictionary-based techniques are used for query correction, then the system is simple to implement, but it cannot capture new phrases, names, abbreviations, and informal terms that appear frequently in web searches
Solution Approach 1:
The patent replaces the static mechanical dictionary-based correction system with a dynamic statistical machine translation system that uses probabilistic models trained on actual search query data. This substitution enables the system to adapt to new phrases, names, and informal terms by learning from user behavior patterns rather than relying on predefined dictionaries.
Solution Approach 2:
The patent introduces dynamic query correction models that are continuously trained and updated using logged search data. The system evolves over time by incorporating new error patterns and correction preferences from users, making it adaptable to emerging search trends and terminology without requiring manual dictionary updates.
2Measurement precision
If statistical machine translation models are used, then query correction accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent performs preliminary actions by pre-training multiple query correction models offline using extensive search data. The models are prepared in advance with learned error patterns and correction preferences, so that during actual query processing, the system can quickly evaluate correction candidates without performing heavy training computations in real-time.
Solution Approach 2:
The patent employs a multi-model approach where several specialized query correction models are trained for different aspects of error correction. Rather than using a single comprehensive model, the system applies multiple models with specific focuses, combining their outputs to achieve high accuracy while managing computational complexity through selective application.
3Measurement precision
If multiple query correction models are combined, then correction accuracy is improved, but the system becomes more complex to manage
Solution Approach 1:
The patent merges multiple query correction models into a unified framework that combines their probabilistic outputs. The system integrates error pattern models, translation models, and language models into a cohesive correction system that leverages the strengths of each individual model while presenting a unified interface for query correction.
Solution Approach 2:
The patent creates a universal query correction framework that can handle multiple types of errors and correction scenarios through a single integrated system. The multi-functional architecture allows the same framework to apply different models based on the specific error patterns detected, providing versatile correction capabilities without requiring separate management systems for each model type.
Data Source
AI summary
Statistical Machine Translation (SMT) based search query spelling correction techniques are described herein. In one or more implementations, search data regarding searches performed by clients may be logged. The logged data includes query correction pairs that may be used to ascertain error patterns indicating how misspelled substrings may be translated to corrected substrings. The error patterns may be used to determine suggestions for an input query and to develop query correction models used to translate the input query to a corrected query. In one or more implementations, probabilistic features from multiple query correction models are combined to score different correction candidates. One or more top scoring correction candidates may then be exposed as suggestions for selection by a user and/or provided to a search engine to conduct a corresponding search using the corrected query version(s).