pain point analysis
When non-English users search with English keywords, the relevance of the results is often reduced due to term translation bias or cultural context differences.
Features of FloatSearch AI
- Bidirectional Terminology Knowledge Base: Over 2 million bilingual terminology cross-references (e.g., ICD code cross-references for medical terminology) are built-in for specialized fields.
- context-awareness: Automatically adjusts term preferences based on the user's location (e.g., a user in Taiwan searching for "program" will also be matched with results related to "program").
- Visualization correction: When there may be translation errors in the input, the interface will display "Do you want to search for: [standardized terminology]?". in the interface when there may be a translation error.
best practice
1. Mark the area of specialization (e.g. computer/medical/legal) in the account settings
2. Use of mixed-language inputs (e.g., "Convolutional Neural Networks CNN Latest Papers")
3. Quickly redirect queries using the "related concepts" tab on the search results page
4. Activate "Encyclopedia of Terms" for quick access to key terms by long press.
This answer comes from the articleFloat: a cross-language intelligent search engine to retrieve knowledge in different languages in their native languageThe
May not be reproduced without permission:AI productivity tools " How to overcome the accuracy challenge in terminology search for non-native English speakers?
