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How are Embeddings Affecting Traditional Text Search?

๐ŸŒˆ Abstract

The article discusses how modern semantic embeddings have impacted traditional text-based search, and the emergence of hybrid search approaches that combine lexical and semantic search techniques.

๐Ÿ™‹ Q&A

[01] Traditional Lexical Search

1. What are the key characteristics of traditional lexical search algorithms?

  • Traditional lexical search algorithms like TF-IDF and BM25 keep track of the vocabulary and word statistics for a set of documents, and then match a user query to the corresponding documents for those words.
  • These algorithms are based on lexical matching, which means they focus on the exact words used in the query and documents.

2. What are the limitations of traditional lexical search?

  • Lexical search can struggle when users don't remember the exact words used in a document, don't know exactly what they're seeking, or want to search for concepts rather than specific terms.

[02] Using Embeddings for Semantic Search

1. What is the core idea behind semantic search using embeddings?

  • Semantic search uses numeric vector representations called "embeddings" to measure the semantic similarity between a user query and documents, even if they use different words.
  • Paragraphs or documents discussing the same topic will have similar embeddings, allowing semantic search to find relevant content beyond just lexical matching.

2. What are the advantages of semantic search over traditional lexical search?

  • Semantic search can find relevant documents that use different vocabulary but discuss the same topic or concepts as the user's query.
  • This helps address the limitations of traditional lexical search when users don't know the exact words used in the relevant documents.

[03] Combining Lexical and Semantic Search as Hybrid Search

1. What is the key idea behind hybrid search?

  • Hybrid search combines traditional lexical search techniques with modern semantic search using embeddings.
  • This allows search engines to leverage the strengths of both approaches, addressing the gaps and limitations of each individual method.

2. What are some ways that hybrid search can be implemented?

  • Hybrid search can be implemented by combining results from separate lexical and semantic search engines.
  • Some search engines are also integrating vector-based semantic search capabilities directly into their lexical search frameworks.

3. What are the benefits of hybrid search compared to pure lexical or semantic search?

  • Hybrid search can provide more comprehensive and accurate search results by leveraging the complementary strengths of lexical and semantic search techniques.
  • It allows users to benefit from both exact phrase matching and semantic concept-based retrieval, addressing a wider range of search needs.
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