I work specifically in this field with clients, and deliver training on applying NLP to search.
You’d be surprised how effective NLP is for use when identifying query intent, and pulling out modifiers that should apply as metadata filters.
Weighted keyword search works a lot, but it fails hard for many long tail queries (especially in e-commerce and other attribute heavy domains).
IMO there really isn’t a good excuse for these firms to fail at queries like this. The query itself isn’t particularly difficult when using a decent NLP stack and following well known practices.
If it's technically possible then presumably it's a deliberate product choice to not have better search results for "shirt without stripes". And that seems entirely plausible.
Google is already by far the most widely used search engine, so they don't really need to innovate or improve the search product very much in order to attract and retain users. Presumably capturing more advertising spending from the companies paying for ads is a bigger priority.
Microsoft under Satya Nadella has been all about enterprise and cloud, and I doubt Bing is a strategic priority any more, so it's not surprising that they wouldn't put a lot of resources into making it better.
Amazon is a little surprising. You'd think they'd have a lot to gain from making it easier for people to find what they're looking for. But maybe less than perfect search results are deliberate? Maybe it's like how supermarkets put basic items in the back of the store and high-margin impulse buys in the front - so you have to walk past chocolates and chips if you want to buy a carton of milk.
If Amazon is deliberately nerfing search results then maybe Google would stand to benefit from having better shopping-related results - people would get frustrated trying to find a shirt without stripes on Amazon and just use Google instead, letting Google profit from advertising in the process. But maybe people selling shirts aren't willing to pay much for ads, so there isn't much money for Google to make by getting better at finding specific types of shirts.
I dunno if any of these conjectures are anywhere near accurate, but it's interesting to think about.
I feel like that's stretching the definition of NLP though. Technically you are processing natural language but it seems like you've found that doing essentially a keyword match but using certain keywords as more advanced filters rather than just search terms.
You’d be surprised how effective NLP is for use when identifying query intent, and pulling out modifiers that should apply as metadata filters.
Weighted keyword search works a lot, but it fails hard for many long tail queries (especially in e-commerce and other attribute heavy domains).
IMO there really isn’t a good excuse for these firms to fail at queries like this. The query itself isn’t particularly difficult when using a decent NLP stack and following well known practices.