Technical Implementation of Semantic Analysis and Query Optimization
Open Researcher's Intelligent Search module uses NLP technology to triple-process raw user input: first, intent recognition, which determines the type of user's research needs by analyzing sentence structure; then query expansion, which automatically adds relevant terms and qualifiers; and finally, the generation of a structured search grammar. For example, when the user inputs 'AI medical application', the system may transform into a precise query of '(Artificial Intelligence OR AI) AND (Healthcare OR Diagnosis) AND (2023..2025)'.
This feature significantly improves the relevance of search results, test data shows that compared to traditional search engines, its first screen results match 62%. The system also has the ability to contextual memory, in multiple rounds of dialogue can continue to optimize the search strategy. Technically, this feature relies on the deep integration of the Firecrawl API and the real-time parsing of search semantics by the Anthropic Claude model.
This answer comes from the articleOpen Researcher: an AI research assistant that analyzes web content in real timeThe
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