Text Editing Apparatus Learning Timing for Conversion Candidates

The system performs preliminary learning at the time of final determination input, storing the character string data before any potential editing occurs. This preliminary action ensures that the learning process captures the intended character string, while the system subsequently monitors for editing operations and triggers re-learning when edits are detected, thus resolving the contradiction between maintaining learning accuracy and reflecting editing changes.

The system implements a feedback mechanism that monitors editing operations performed on finally determined character strings. When an edit is detected, the system feeds back this information and automatically triggers re-learning of the edited character string, ensuring that subsequent conversion candidates reflect the latest edits. This feedback loop resolves the contradiction by dynamically updating the learning database based on actual user modifications.

The system performs preliminary learning at the time of final determination, capturing the character string data before editing. When the same character string is needed again, the system retrieves it from the learning database, and if edits are made, automatically triggers re-learning to update the database. This eliminates the need for operators to re-edit character strings manually, thus resolving the contradiction between text creation efficiency and re-editing time loss.

The system provides self-service by automatically detecting editing operations and triggering re-learning without requiring operator intervention. The system monitors itself for edits, automatically updates the learning database, and makes the edited character strings available for future conversions. This self-updating mechanism resolves the contradiction by eliminating manual re-editing while maintaining productivity.

The system implements feedback monitoring that detects when editing operations are performed on finally determined character strings. When an edit is detected, the system automatically triggers re-learning to capture the edited version and update the learning database. This ensures that editing information is retained and reflected in subsequent conversion candidates, resolving the contradiction between text modification flexibility and editing information retention.

The system performs self-updating by automatically detecting edits and triggering re-learning without operator intervention. The system monitors its own learning database, detects when character strings are modified, and automatically updates the database with the edited versions. This self-maintaining mechanism resolves the contradiction by ensuring editing changes are preserved and reflected in future conversions while maintaining adaptability.