Which mode is aimed at improving the recall of entities?

The mode that is aimed at improving the recall of entities is associated with checking an entity that has been classified. This process involves validating and refining the extraction accuracy of named entities within a given context, allowing the model to better recognize and recall specific entities in future analyses. By engaging in this check, users can provide feedback to the system, which helps the underlying model adjust and improve its ability to identify those entities accurately over time.

Each of the other choices serves different purposes. For instance, a missed entity may involve focusing on entities that weren’t recognized during previous extractions, but this does not directly enhance recall in the context of the entities that were already identified. Teaching an entity relates to introducing new classifications or refining entity definitions rather than addressing recall. Checking labels is more concerned with validating the correctness of labeled data rather than aiming at improving how well the entities are recognized across instances.