Case-insensitive matching is only one step in finding duplicate text. An AI model needs more rules to identify text that is similar but not exactly the same. A developer must define what counts as a duplicate for the AI model.
By Pawan Nayar · from the LLOS AI Learning record
Case-insensitive matching is only one step in finding duplicate text.
An AI model needs more rules to identify text that is similar but not exactly the same. A developer must define what counts as a duplicate for the AI model.
Case-insensitive matching fixes only one type of duplicate. It finds 'Coffee' and 'coffee' as the same. It does not find 'traffic jam' and 'traffic jams' as the same. It also does not find 'colour' and 'color' as the same. An AI model sees these as different pieces of text. A human sees them as duplicates.
A developer builds a system to collect customer feedback. The developer uses case-insensitive matching. The system still shows 'slow service' and 'slow services' as two different issues. The developer must add more rules to group these similar phrases.
Resolved from this lesson's own record — real definitions, not invented for this page.
More from the same record, pulled by shared topic — not written new for this page.
Sometimes a computer program finds words that are not useful. These can be parts of a website like "submit" or "close".
A developer must check all slogans in the AI model's training data. This check happens in two steps.
The AI model uses a special format called `sentence_splitter`. This format helps the AI model find missing sentence endings.
The AI model first breaks text into small pieces. It classifies these pieces.
Case-insensitive matching is only one step in finding duplicate text. An AI model needs more rules to identify text that is similar but not exactly the same. A developer must define what counts as a duplicate for the AI model.