Multilingual Query Generation via User Content Analysis
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Solution Overview
Problem
Current search engines face challenges in accurately determining the languages a user is interested in searching, as morphological analysis based solely on a submitted query can produce limited or incorrect results due to typos, incomplete words, and browser language settings not reflecting the user's familiarity or intended search languages.
Innovation Solution
A system that monitors user-generated content to detect language settings and preferences, compiles a list of preferred languages with scores, intercepts queries to analyze them in multiple languages, and generates multilingual queries to improve search results by boosting relevant content based on language proficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If morphological analysis is performed based solely on submitted query, then query processing speed is maintained, but language detection accuracy deteriorates due to typos, incomplete words, and browser settings not reflecting user intent
Solution Approach 1:
The system performs preliminary language detection by analyzing user-generated content (posts, comments, articles) before the user submits a search query. This advance preparation creates a language profile that accurately reflects the user's preferred languages, eliminating the need for complex real-time analysis of potentially flawed query text or browser settings.
Solution Approach 2:
The system automatically monitors and analyzes user-generated content to self-determine the user's language preferences without requiring explicit user input or manual configuration. The language detection mechanism serves itself by using the user's own content creation patterns as the basis for determining search language preferences.
2Reliability
If multiple languages are analyzed for each query, then search result relevance is improved, but query processing time increases
Solution Approach 1:
The system pre-determines the user's preferred languages and their relative importance rankings before query submission. This preliminary setup allows the query processing system to efficiently focus analysis on only the relevant languages for each user, avoiding the time-consuming analysis of all possible languages while maintaining high search result relevance.
Solution Approach 2:
The system applies different language analysis priorities based on user-specific preferences rather than treating all languages equally. By assigning different weights and priorities to different languages based on the user's demonstrated content creation patterns, the system optimizes processing time by focusing computational resources on the most relevant languages for each individual user.
3Measurement precision
If browser language settings are used to determine search language, then implementation simplicity is maintained, but accuracy deteriorates as settings may not reflect user familiarity or intended search languages
Solution Approach 1:
Instead of relying on browser settings or explicit user input, the system automatically determines language preferences by analyzing the languages in which users create content. This self-service approach extracts language preference information directly from user behavior patterns, providing high accuracy without requiring complex implementation of multiple detection methods.
Solution Approach 2:
The system uses user-generated content as an intermediary indicator to infer language preferences. Rather than directly querying browser settings or asking users, the system analyzes the linguistic characteristics of content created by users as an indirect but more accurate measure of their actual language proficiency and search preferences.
Data Source
AI summary
Examples of techniques for performing multilingual queries are described herein. A computer-implemented method includes monitoring, via a processor, a client for language settings during user-generated content creation and detect one or more language settings. The method includes analyzing user-created content to detect a language of a text. The method includes compiling a list of scored preferred languages for the client based on the detected language settings and the detected language of the text. The method includes intercepting a query from the client. The method includes analyzing a text of the intercepted query in a plurality of selected languages based on a language setting of a user interface application, a detected language of the query, and a predetermined number of preferred languages of the client to produce results of analysis for each selected language. The method includes generating a multilingual query based on results of analysis for the selected languages.