Minimum Number of Training Examples Needed to Feed Supervised Text Analytics?
Minimum number of training examples needed to feed supervised text analytics?
When it comes to a supervised text analytics, is there a minimum number of training examples that needed to be fed to the AI engine?
There has been huge progress made in the last years. If you have heard and have had an opinion formed a few years ago, It’s not true anymore. Actually there are supervised learning systems, which are already categorizing from scratch quite well. You really need to teach them only the specifics of your business and the context of your very question. Typically when it comes to B2B, but also and certainly B to C domains. If it comes to product names or some abbreviation or slang or sometimes it’s also the context and even sarcasm, everything can be trained. Sometimes also the answer depends on the question, that’s why it always needs some training be perfect and human equivalent. When we look at our projects at CX.AI, we typically teach 300, 500 or max 700 examples. Then the machine can categorize infinite examples automatically.
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