Semantic Analysis
In natural language processing, semantic analysis interprets meaning in text, such as who did what, which entities are involved or what a request asks for. Its outputs can be labels, relationships or other meaning representations.
Consider “Cancel the renewal for Acme, but keep the trial account.” A system may identify a cancellation intent, the affected customer and the exception. Those interpretations can become structured fields that a workflow checks before taking action.
Meaning involves more than syntax or word overlap. In “Acme bought Beta,” the buyer and purchased company have different roles; reversing them changes the claim. Semantic role labeling is one approach to representing such relationships.
Semantic search retrieves related information; semantic analysis interprets a specific input for a task. Both can use learned representations. This matters for evaluation: fluent text or a similar passage does not prove that the extracted account, intent and exception are correct. Define the fields, test them separately and provide a way to handle ambiguity before a consequential action.
Sources
- Jurafsky and Martin: Semantic Role Labeling and Argument Structure — Explains semantic roles, predicates, arguments and meaning representations beyond surface syntax.
Go deeper
- spaCy: Linguistic features docs
Try named-entity recognition and structured linguistic annotations, and inspect their limitations.