Reproduce “TinySearch -- Semantics based Search Engine using Bert Embeddings” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Manish Patel
TinySearch, developed at Texas A&M University, is a semantics-oriented search engine that leverages BERT embeddings and a neural network to understand query meaning beyond keywords. The system achieved an F1-score of 0.8 for complex queries, indicating improved relevance for intricate information retrieval tasks.
Key insights
- BERT embeddings effectively capture the nuanced semantic meaning of both queries and documents.
- Training a dedicated neural network to compute similarity between BERT embeddings can capture more complex relationships than simple cosine similarity.
- Semantic search, powered by contextual embeddings and learned similarity, yields improved relevance, particularly for complex and long-tail queries.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.