DiceTechJobs/ConceptualSearch

Train a Word2Vec model or LSA model, and Implement Conceptual Search\Semantic Search in Solr\Lucene - Simon Hughes Dice.com, Dice Tech Jobs

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Emerging

This project helps search engine managers improve the relevance and recall of their search results by moving beyond simple keyword matching. By analyzing your organization's specific documents, it learns the underlying concepts and relationships between words. This allows your search engine to understand the intent behind a search query and provide more accurate results, even if the exact keywords aren't present. It takes your existing document corpus as input and outputs synonym files that can be directly integrated into search platforms like Solr.

259 stars. No commits in the last 6 months.

Use this if you are managing a search engine for a specialized domain and need to enhance search quality by enabling conceptual or semantic search, rather than just keyword matching.

Not ideal if your search needs are very basic, you don't have a specific document corpus to analyze, or you are not using a search engine that supports synonym file integration.

search-engine-optimization information-retrieval content-management enterprise-search knowledge-management
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 10 / 25
Maturity 16 / 25
Community 22 / 25

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Stars

259

Forks

54

Language

Jupyter Notebook

License

Apache-2.0

Last pushed

Apr 26, 2019

Commits (30d)

0

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