Keyword versus semantic search: test exact wording and natural descriptions separately
Compare lexical matching with semantic similarity, explaining how paraphrases, proper names and index maintenance influence document retrieval.
Choose an approach
Keep keyword search as a baseline when original identifiers or specialized words are known. Consider semantic search when users remember purposes but sources use different wording. Semantic search has several implementations; this article focuses on lexical retrieval versus vector-based semantic retrieval. They can be combined. First decide whether the query needs an exact object or related candidates.
Lexical matches and similar meanings use different evidence
Keyword retrieval uses text indexes and analysis rules; vector retrieval compares embedding distances. Microsoft documents these capabilities separately. A result without the query’s words can be semantically relevant without referring to the same product model.
Chinese tokenization, abbreviations and spelling affect lexical matches, while embedding models and indexed material affect similarity. Confirm that complete content is indexed before comparing methods; otherwise missing documents may be incorrectly described as proof that a search technique is useless.
Test exact and descriptive queries on identical documents
For the same collection, prepare original-word and paraphrased queries, such as a product identifier and its purpose. Keep filters and result limits identical and check expected documents. Also record ranking and false matches when many results appear instead of accepting one successful search.
Add similar wording with different meanings and similar meanings referring to different objects. Have maintainers judge against originals instead of asking a model to rationalize results. These checks support selection; no index or measured retrieval performance is reported here.
Choose combinations based on changes and maintenance capacity
Keyword systems maintain analysis rules and searchable fields. Vector systems additionally maintain embedding versions and regeneration. Existing vectors generally cannot simply be mixed with queries from a new embedding model. Include reindexing after content updates, not just query charges.
If both query types matter, retain their results and evaluate fusion. If a task only resolves exact identifiers to records, explicit fields and equality queries may suit it better. Base choices on query needs rather than assuming the semantic label always beats keyword search.
Checked sources
These sources support the concepts and methods described here. Examples and test scope are labeled separately; check the version of the tools you use.