Multilingual Search Index Segmentation for Linguistic Accuracy
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Solution Overview
Problem
Current multilingual search engines face complexity in translating queries and documents across languages, as stemming methods fail to address linguistic variations such as gender, verb conjugations, and diacritical marks, leading to inaccurate search results.
Innovation Solution
Creating multiple language-specific indices with stemmed and non-stemmed terms, translating queries into target languages using pre-translated documents, electronic dictionaries, and automated translators, and processing these translations against monolingual indices to identify relevant documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If stemming methods are used to simplify search terms, then search processing is simplified, but linguistic nuances such as gender, verb conjugations, and diacritical marks are lost
Solution Approach 1:
The patent segments the search index into multiple language-specific indices, each maintaining distinct linguistic features. Instead of using a single unified index with generic stemming, each language index preserves language-specific morphology, gender, and diacritical marks while still benefiting from stemming where appropriate for that language.
Solution Approach 2:
The patent applies local quality by allowing different stemming and normalization strategies for different languages. Each language index can apply language-appropriate stemming rules while preserving linguistically significant features like gender in Romance languages or verb conjugations in Germanic languages, rather than applying a uniform stemming approach.
2Adaptability or versatility
If queries are translated into target languages for multilingual search, then search coverage expands, but translation accuracy decreases due to linguistic conflicts
Solution Approach 1:
The patent segments the document collection into language-specific indices, allowing queries to be processed against the appropriate language index. This segmentation enables accurate translation and matching by maintaining separate linguistic contexts, preventing translation errors that would occur in a unified multilingual index.
Solution Approach 2:
The patent uses language-specific indices as intermediaries between the query and the document collection. Instead of directly translating queries across all languages, the system translates queries into target languages and processes them through appropriate language-specific indices, which act as mediators that preserve linguistic accuracy.
3Measurement precision
If multiple language-specific indices are created with both stemmed and non-stemmed terms, then search accuracy improves, but system complexity increases
Solution Approach 1:
The patent merges stemmed and non-stemmed terms within each language-specific index, allowing the system to leverage both approaches. The index structure combines language-specific term variants with their stemmed forms, enabling accurate matching while maintaining manageable organization through unified index structures rather than separate indices for each term type.
Data Source
AI summary
The present inventors have a devised one or more novel methods, systems, and interfaces for facilitating multi-lingual searches. One exemplary method entails creating multiple language-specific indices for a collection of documents, with each index including stemmed and non-stemmed versions of terms from the documents. Users submit queries that are associated with a set of one or more target languages. Query processing entails translating original and stemmed versions of each term in a query into each of the target languages, using one or more techniques that each yield a set of potentially equivalent query terms. Each set of potentially equivalent query terms is then processed against the corresponding language-specific index, using a conventional monolingual search technique, such as a Boolean or natural language query, to identify documents from the collection. The resultant documents are presented to the user in language groupings or by computed relevance.