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The point of this isn't asking how to apply boolean search operators, it's showing that the largest AI-focused companies in the world absolutely suck at NLP.
Why would you really apply NLP to a search engine though? Generally speaking a weighted keyword search is good enough 95% of the time and requires significantly less resources to perform.
Weirdly, searching for 'shirt no stripes' on Google returned this web page at top of the organic results. So at some point, searching for a shirt online will involve this conversation. Even more confusing. (Although I expect my filter bubble will play a part in that)
because google does understand that no and without are interchangeable. But, understandably, it does not correlate "shirt without stripes" as being the same thing as "solid-colored shirts." Why, because no one advertises or describes a solid-colored shirt as a shirt without stripes and no one searches that way. It's an irrelevant point, in my opinion.
''' Shirt Without Stripes | Hacker Newsnews.ycombinator.com › item 42 mins ago - The point that the author is making, in a very understated way, is that all three companies have PR websites that breathlessly describe their ...
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The point of this isn't asking how to apply boolean search operators, it's showing that the largest AI-focused companies in the world absolutely suck at NLP.
Why would you really apply NLP to a search engine though? Generally speaking a weighted keyword search is good enough 95% of the time and requires significantly less resources to perform.
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.
The correct query would have been "shirt -stripes". That works fine, or at least does on Google. But yeah, sentence parsing fail.
I would never search for something this way. If I wanted to find a 4WD car, I wouldn't search for "cars without 2WD." Likewise, here, I would search for solid-colored shirts. And these services are limited to the content/terminology utilized by the cataloged sites/products. If I am selling a "black shirt" or a "solid black shirt," it is not google's job to catalog it as a "shirt without stripes," unless I advertise i…
Unfortunately people don't search for "solid shirts". At best they search for "plain shirts", but there's a lot of taste to clothing that means people often do want a shirt without stripes, but are open to patterned/plain.
I think searching "shirts without stripes" is very legitimate in fashion.
I say this having built a clothing search function for the company I work for, and one that does not support this sort of query.
The point that the author is making, in a very understated way, is that all three companies have PR websites that breathlessly describe their advanced AI capabilities, yet they cannot understand a very simple query that young children can.
EDIT: scrap that, I didn't mean Alexa, which is doing AI obviously, but the search engine of Amazon's retail website.
Anyway, NLP is hard and everyone sucks at it. Think about it: just building something that could work with any or any other way to express the same requests would mean understanding the relationships of every possible combinations of N1 and N2. It means building a generalized world model that is quite different from simply applying ML to a narrow use case. Cracking that would more or less mean solving general AI which probably won't happen soon.