Natural Language Processing: Brittle and Spurious

August 24, 2018

I read “NLP’s Generalization Problem, and How Researchers Are Tackling It.” From my vantage point in rural Kentucky, the write up seems to say, “NLP does not work particularly well.”

For certain types of content in which terminology is constrained, NLP systems work okay. But, like clustering, the initial assignment of any object determines much about the system. Examples range from jargon, code words, phrases which are aliases, etc. NLP systems struggle in a single language system.

The write up provides interesting examples of NLP failure.

The fixes, alas, are not likely to deliver the bacon any time soon. Yep, “bacon” means a technical breakthrough. NLP systems struggle with this type of utterance. I refer to local restaurants as the nasty caballero, which is my way of saying “the local Mexican restaurant on the river.”

I like the suggestion that NLP systems should use common sense. Isn’t that the method that AskJeeves tried when it allegedly revolutionized NLP question answering? The problem, of course, was the humans had to craft rules and that took money, time, and even more money.

The suggestion to “Evaluate unseen distributions and unseen tasks.” That’s interesting as well. The challenge is the one that systems like IBM Watson face. Humans have to make decisions about dicey issues like clustering, then identify relevant training data, and index the text with metadata.

Same problem: Time and money.

For certain applications, NLP can be helpful. For other types of content comprehension, one ends up with the problem of getting Gertie (the NLP system) up and running. Then after a period of time (often a day or two), hooking Gertie to the next Star Trek innovation from Sillycon Valley.

How do you think NLP systems handle my writing style? Let’s ask some NLP systems? DR LINK? IBM Watson? Volunteers?

Stephen E Arnold, August 24, 2018


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