Vector search finds content that sits near your query in an embedding space. That makes it good at “related to.” It does not, on its own, guarantee “the sense you meant.” A query containing “bank” can sit close to loans, riverbanks, pool shots and collections of things, and nothing in the proximity score says which one you wanted. Hybrid retrieval, which combines vector and keyword search, helps with a different problem: exact terms. It is not a built-in sense disambiguator.
The problem: one word, several unrelated meanings
The textbook Introduction to Web Search Engines puts it plainly: “Polysemy refers to words with multiple meanings.” Its example is “bank,” which can mean a financial institution, the edge of a river, a pool shot, or a collection of things (excerpt). A retrieval system needs enough context to prefer the intended sense, and a one-word or short query often does not supply it.
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What vector proximity does and doesn’t tell you
Google Cloud describes embeddings as positions in a vector space that represent the meaning of content. Vector search then returns the embeddings nearest to the query (Google Cloud). Nearness is a retrieval signal. It tells you the content is semantically related to the query. It is not an explicit guarantee that the system resolved an ambiguous word to the sense you intended.
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Context in the query (“river bank erosion” versus “bank account fees”) usually does the real work. When the query is short or the corpus mixes domains, the results can blend senses. The reviewed sources do not publish a benchmark for the “bank” case, so how often this happens depends on your model and corpus.
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Where vector search is weak: exact terms
Google Cloud notes that semantic search can only find data the embedding model can make sense of. It names arbitrary SKUs, newly added product names and proprietary codenames as cases where keyword retrieval helps. Microsoft similarly points to product codes, specialized jargon, dates and people’s names as situations where exact matching matters (Microsoft Learn).
What hybrid search adds
Hybrid search runs semantic and keyword (full-text) retrieval together and merges the results.
- Azure AI Search: runs full-text and vector queries in parallel and merges the ranked lists with Reciprocal Rank Fusion (RRF). Microsoft says benchmark testing indicates hybrid retrieval with its semantic ranker can improve relevance, but the passage gives no figure, dataset or date (source).
- OpenSearch: hybrid search arrived in version 2.11. It offers score-based normalization or rank-based RRF for combining keyword and semantic queries (docs).
- Google Cloud: explains dense and sparse embeddings and hybrid retrieval with rank fusion (docs).
Hybrid gives you coverage across two kinds of evidence: conceptual similarity and literal matching. A keyword leg can anchor a query on a literal term such as “riverbank” or a specific bank’s name. But a keyword leg also matches “bank” in every sense, so hybrid alone does not pick one. Choosing the intended sense may still need query reformulation, metadata or filters (for example, a domain or category field), or a later ranking stage. None of the reviewed sources claim a universal accuracy guarantee for ambiguous queries.
How to test it on your own corpus
This is an evaluation approach inferred from how the retrieval signals differ, not a result from the sources.
- Collect real ambiguous queries from your domain, such as “bank,” “bank charges,” and “bank near the fishing spot.”
- For each, label which sense a reader meant.
- Run semantic-only and hybrid retrieval and inspect the top results for sense match.
- Include exact-term queries (SKUs, names, codenames, dates) to see where each approach fails.
- Try your fusion options, normalized scores versus RRF, and compare results on your data.
| Axis | What to check |
|---|---|
| Conceptual matching | Does it find relevant items when wording differs? |
| Exact-match sensitivity | Are names, codes and jargon preserved? |
| Result fusion | How does score normalization or rank fusion change results on your corpus? |
| Intended-sense relevance | Do top results match the sense the user meant? |
The Bottom Line
Treat vector similarity as “related,” not “correct sense.” Add keyword matching for exact terms, and add context, filters or reranking for ambiguity, then verify with your own ambiguous queries.
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