Pretty much every entity detector that I have used operates on a similar principle. Not sure whats new here.
How Alexa knows “peanut butter” is one shopping-list item, not two
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Re: How Alexa knows “peanut butter” is one shopping-list item, not two
#12The German language is superior in this regard because there it would we either "peanut, butter" or "peanutbutter".
How is this relevant? Peanut butter isn't spoken like "peanut, butter" it's spoken like "peanutbutter".
Re: How Alexa knows “peanut butter” is one shopping-list item, not two
#13This only a surprising thing because a human would parse the waveform as two words and then back into a single concept. A computer could parse it as a single concept directly or in syllable lengthed chunks to be reconfigured however.
Re: How Alexa knows “peanut butter” is one shopping-list item, not two
#14The German language is superior in this regard because there it would we either "peanut, butter" or "peanutbutter".
I'd say it was probably worse in this respect, doesn't German have a lot more of these "compound" words that would need to be parsed in this way?
Re: How Alexa knows “peanut butter” is one shopping-list item, not two
#15Pretty much every entity detector that I have used operates on a similar principle. Not sure whats new here.
Have you often seen adversarial training used for sequence labeling to improve generalization across domains? The LSTM-CRF model appears to be the same model proposed by Lample et al (2016). I’d agree that it is common now to use that architecture.
Re: How Alexa knows “peanut butter” is one shopping-list item, not two
#16This, yet again shows just how far the computer science is from real natural language processing, despite of all "AI" companies' claims.
Unless they have all and everything hardcoded, even such natural thing are impossible to process for the "natural" language processing programs.
All cloud "AI" and "natural" language processing services should really be called "lots and lots of hardcoded stuff language processing"
Re: How Alexa knows “peanut butter” is one shopping-list item, not two
#17The German language is superior in this regard because there it would we either "peanut, butter" or "peanutbutter".
I'd say it was probably worse in this respect, doesn't German have a lot more of these "compound" words that would need to be parsed in this way?
Exactly, car insurance is much better than Kraftfahrzeug-Haftpflichtversicherung.
Re: How Alexa knows “peanut butter” is one shopping-list item, not two
#18This, yet again shows just how far the computer science is from real natural language processing, despite of all "AI" companies' claims. Unless they have all and everything hardcoded, even such natural thing are impossible to process for the "natural" language processing programs. All cloud "AI" and "natural" language processing services should really be called "lots and lots of hardcoded stuff language processing"
State of the art virtual assistants offer little intelligence over a command line interface, except instead of typing the command line in, you say it. Besides that, not much difference; the syntax is rigid and the computer doesn't understand your utterance more intelligently than GCC understands "gcc -o my_prog my_prog.c".
Re: How Alexa knows “peanut butter” is one shopping-list item, not two
#19Pretty much every entity detector that I have used operates on a similar principle. Not sure whats new here.
While these types of blogs might not be revolutionary, they're still useful to people new to the subject who might just be getting into search or are having to implement lite search functionality into their applications.
I'm actually working on an application now where the initial spec called for "search" and it was implemented as exact token matching. A bug was immediately filed because searches for "wlk", "walk", and "walk event" all returned different results.
Re: How Alexa knows “peanut butter” is one shopping-list item, not two
#20This only a surprising thing because a human would parse the waveform as two words and then back into a single concept. A computer could parse it as a single concept directly or in syllable lengthed chunks to be reconfigured however.
A human really wouldn't. It's the same thing as telling the difference between black bird and blackbird in running speech; peanut butter may be spelled as two words, but it's spoken as a single thing. If the concept was new enough that you were consciously talking about a type of butter made from (of all things) peanuts, it would be a black bird vocal entity rather than a blackbird one.