Similarly, for NLP – the generic extraction can be very frustrating. Watson can tell you all the topics, entities and proper nouns that it spots but without a context, it is not helpful.
Let’s say you want to analyze a lot of product reviews using Watson. It is likely to pick up stuff that is not deep enough and very generic. Whereas, we pull out some amazing insights from reviews to automate merchandising for Retailers. Take a look at this:
We break down product reviews into multiple areas of interest – how do customers use it, what do they use it for, what are some of the other products that they use it with, what do they like about the performance of the product.
Even within Retail, this model changes from blenders to shoes:
Imagine if we were to show activity filters for Coffee Makers? Our customers will be really disappointed.
And that’s the challenge with Watson and other generic platforms. It is built as a platform that has fundamental capabilities and it lets you build custom solutions on top. If you try to use the generic capabilities, you would be disappointed.