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Old school NLP has always fascinated me though, and I'm pretty excited about what might be possible in the future by using more than purely statistical methods for accomplishing NLP tasks. Maybe the author could have speculated more wildly in his prognostication ;)
We'll start to see the re-emergence of tools from old-school NLP, but now augmented with the powerful statistical tools and data-oriented automation of new-school NLP. IBM's Watson already does this to some extent. This is not a new trend. As early as 1997, Steven Abney augmented [1] attribute-value grammars with discriminative modelling (maximum entropy models) in this case to form 'stochastic attribute-value gramma…
"We can't buy any bread because we haven't got any bread."
And it's not just English. In Chinese one is taught that "ni hao ma?" is the greeting equivalent to "hello, how are you" but try it on a Chinese person and it amuses them. My Chinese friend at Uni says that Chinese people use "Ni chi ma?" which is literally "have you eaten?" (although we both appreciate that is a bit of a generalisation for 1bn people).
We'll start to see the re-emergence of tools from old-school NLP, but now augmented with the powerful statistical tools and data-oriented automation of new-school NLP. IBM's Watson already does this to some extent. This is not a new trend. As early as 1997, Steven Abney augmented [1] attribute-value grammars with discriminative modelling (maximum entropy models) in this case to form 'stochastic attribute-value gramma…
The problem is slippery though, people always reuse words to express different ideas : "We can't buy any bread because we haven't got any bread." And it's not just English. In Chinese one is taught that "ni hao ma?" is the greeting equivalent to "hello, how are you" but try it on a Chinese person and it amuses them. My Chinese friend at Uni says that Chinese people use "Ni chi ma?" which is literally "have you eaten?…
I get the impression that while it is true the computational side of computational linguistics has seemingly seen more attention for lucrative reasons, but now it is seeing some success there more people trying to incorporate more from the linguistic side, when it doesn't cause for a huge amount of computational expense.
It doesn't seem like anything new, however, that business needs drive funding for particular areas in academia. Sadly, more so than ever considering the greed of the school systems (but that is another topic).
[1] https://digital.lib.washington.edu/researchworks/handle/1773...
Earlier quoted context omitted.
The problem is slippery though, people always reuse words to express different ideas : "We can't buy any bread because we haven't got any bread." And it's not just English. In Chinese one is taught that "ni hao ma?" is the greeting equivalent to "hello, how are you" but try it on a Chinese person and it amuses them. My Chinese friend at Uni says that Chinese people use "Ni chi ma?" which is literally "have you eaten?…
The Taiwanese say "ni hao ma". That's probably where pedagogy got it from.
The point being though, that even if you are taught "Hello, how are you" in an English class then you could well be stumped when you come to my region and we say "How's tricks?"
The beginning of the article reminds me of the quote : "Every time I fire a linguist, the performance of our speech recognition system goes up." But nowadays statistical NLP systems regularly use syntactic and semantic information as a features in the learning phase.
This is a pretty good explanation of why almost all practical applications of NLP are now accomplished by statistics rather than fancy linguistic grammar models you might have read about in a Chomsky book. Old school NLP has always fascinated me though, and I'm pretty excited about what might be possible in the future by using more than purely statistical methods for accomplishing NLP tasks. Maybe the author could ha…
But: I'm building an SDK for conversational AI (think Siri, in any app, and 10 times better), that's what the site as a whole is for. I think in 5 years it'll be pretty commonplace to have fairly natural, Jarvis-like conversations with computers, and within 10 years we'll have R2D2/C3PO robots.
Reading this article, particularly the part about sentiment analysis, was interesting to me because last year I did my thesis[1] regarding sentiment classification using a somewhat mixed approach (albeit pretty simple) where I factored in basic sentence structure in addition to word features to see improvement in accuracies. I found it really neat to see various cases where particular sentence structures like PRP RB…
"but with the advent of computers, it became possible to monetize NLP and the priority shifted to making products people would buy, rather than a system that was scientifically correct."
The competition for a NLP computer program is not another NLP computer program, but call centers in India, Phillipines, onshore prison labor, that kind of "support"
Reading this article, particularly the part about sentiment analysis, was interesting to me because last year I did my thesis[1] regarding sentiment classification using a somewhat mixed approach (albeit pretty simple) where I factored in basic sentence structure in addition to word features to see improvement in accuracies. I found it really neat to see various cases where particular sentence structures like PRP RB…
Yep, I agree with what you say. Tho I would dispute calling a POS gloss much of a structure. When I think structure, I think full syntax. Parse + formal features. Or a full logical formula for the semantics! Now that's structure!