Dynamic Query Tokenization for Unknown-Language Search Terms
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Solution Overview
Problem
Existing index-based search engines struggle with unknown language detection in multi-language search requests, leading to inaccurate and less relevant search results when applying incorrect tokenization rules.
Innovation Solution
A multi-pass tokenization approach is employed to dynamically detect the language of search terms, applying global and language-specific rules based on confidence levels, ensuring accurate tokenization and relevance in multi-language searches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If language-specific tokenization rules are applied, then search accuracy is improved, but the system fails when language is unknown
Solution Approach 1:
The system dynamically adapts tokenization rules based on detected language. Instead of using static language-specific rules that fail when language is unknown, the system implements dynamic language detection that selects appropriate tokenization rules at runtime, allowing the system to handle both known and unknown languages effectively
Solution Approach 2:
The system creates a universal tokenization framework that can handle multiple languages through a single multi-language tokenizer. This tokenizer incorporates both language-specific rules and language-agnostic rules, making it capable of processing text in any language without requiring separate tokenizer implementations for each language
2Measurement precision
If language detection is performed, then tokenization accuracy is improved, but processing time increases
Solution Approach 1:
The system performs partial language detection by analyzing only sufficient portions of the text to identify language characteristics. Rather than performing exhaustive analysis on entire documents, the system detects language from representative samples or key segments, achieving adequate tokenization accuracy with reduced processing overhead
Solution Approach 2:
The tokenization process is segmented into multiple passes: first applying language-agnostic tokenization rules that work for all languages, then selectively applying language-specific rules only after language detection. This segmentation allows the system to minimize the use of computationally intensive language detection while still achieving accurate tokenization when possible
Data Source
AI summary
Devices, systems, and methods for tokenizing search attributes and terms of a search query for an index-based search. A method may include receiving, by a search service of a provider network, a first search query to search a first searchable document set, the first search query including a first search term in a first language; applying a first tokenization rule to identify the first search term in the first search query; determining that the first search term is in the first language; applying a second tokenization rule to tokenize the first search term based on the first search term being in the first language; causing a launch of a search instance by a managed compute service of the provider network, the search instance to execute a search function for a keyword-based text search using the tokenized first search term.