Shirt Without Stripes

Post by jjnoakes » Mon, Apr 20, 2020, 6:49 PM UTC

shanecleveland wrote: post #92

lifeisstillgood wrote: post #71

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.

Post by onurcel » Mon, Apr 20, 2020, 6:51 PM UTC

DenisM wrote: post #197

This problem is known as "attribution" - you have a "no" or "without" in the sentence, but you don't know where it belongs. One could (and one does) argue that the problem cannot be solved with statistical methods (ML), especially not in any domain where accuracy is required, such as medical recored analysis: "no evidence of cancer" and "evidence of no cancer" are very different things. Zooming out, the language fiel…

Post by worik » Mon, Apr 20, 2020, 6:53 PM UTC

londt8 wrote: post #272

animalCrax0rz wrote: post #259

Earlier quoted context omitted.

"there is no evidence of cancer" and "there is evidence of no cancer" are two different statements with different meaning, so it's more complex a task than just understanding the importance of "no" in a sentence. It's involves semantic analysis of the sentence. The paper I linked to below describes a technique they call "deep parsing." Check it out for more context.

I tried to search for "cheese without holes" on Google and it yielded good results. I think the problem here is that the query is something people would rarely search.

Post by tsukurimashou » Mon, Apr 20, 2020, 6:56 PM UTC

packetlost wrote: post #88

meritt wrote: post #13

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.

Post by js2 » Mon, Apr 20, 2020, 6:57 PM UTC

derivativethrow wrote: post #288

smoyer wrote: post #266

Earlier quoted context omitted.

While this is indeed an example of the attribution problem, I'd argue that this particular query will never be solved. I don't search for a "shirt without stripes", I search for a "solid shirt, or a " hawai'ian shirt". I'd be curious to see how many sentences with attribution problems actually have other structural issues. If I want to write clearly and without ambiguity, I rewrite sentences that have these problems.…

Consider the query, "non-glass skyscrapers", which suffers from the same problem. What do you call a skyscraper like that if you want to refer to it? They exist, but you can't find them using that search term on Google.