
Watch a machine-learning system parse the grammatical structure of sentences - hbrid
https://foxtype.com/sentence-tree
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jonathankoren
So FoxType is just Spacy[0]? I honestly thought this might be something
different until I checked out the Spacy page and saw FoxType listed as a
customer. Kind of a dick move simply repackaging other people's work and
passing. It off as your own. (Yes, this is literally just the "displacy"
demo.)

Spacy is a pretty good system. It's fast, way faster thank NLTK and the
Stanford stuff and it's pretty accurate. Also it's licensing is pretty
flexible. The only knock I have against it is the lack of bindings for
languages other than Python, but whatever.

[0] [https://spacy.io/](https://spacy.io/)

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foota
There's more to their product it seems than just the front end for sentence
diagramming. While a shout out wouldn't hurt, I don't think it's really a dick
move.

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jonathankoren
There's also more to Spacy than just dependency parsing as well, but that's a
big part of it.

I would certainly hope there's more to FoxLab, but I have literally seen this
demo before.

I have also been around the block enough times to know that putting some ill
fitting duct tape around a bunch of other libraries that do all the work to
make a quick buck in a hot space is a very common trick as well.

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guidedlight
Whilst an impressive demonstration, how is it an example of machine learning?
It looks like lexical analysis to me.

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knn
Same thoughts as mine. Seems like ML was just thrown in the title to get more
hits.

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ma2rten
It uses machine learning to parse the sentence, all modern lexical parsers are
based on machine learning.

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nevir
Aren't most of them rule based?

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jonathankoren
Not for years. State of the art has moved on from Eric Brill's dissertation 25
years ago.

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Vivtek
Seems to fall for a lot of easy traps. Garden path sentences ("The old man the
boats" or "The horse raced past the barn fell") are parsed wrong, and it
parses the two sentences/phrases "When he did that I laughed" and "When he
said that I laughed" identically - by interpreting "that" as a preposition in
both cases, which it isn't.

It's cool that it parses on the fly. But unless it parses _correctly_ I don't
see how it actually serves a purpose other than just looking cool.

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DonaldFisk
These require a system which either backtracks or recognizes it's made a
mistake and corrects it. I know how to do that with a rule-based parser.

It also fails on "British left waffles on Falklands." While there are two
syntactic parsers for that, one of them borders on the absurd and should be
rejected. The problem is that to parse some sentences requires understanding
of the words and phrases it reads, which in turn requires common sense.

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Vivtek
Well, that is just choosing the semantically correct parse from multiple
syntactically correct parses. This parser isn't even finding syntactically
correct parses.

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mark_element
It doesn't do well on the classic:
[https://en.wikipedia.org/wiki/Buffalo_buffalo_Buffalo_buffal...](https://en.wikipedia.org/wiki/Buffalo_buffalo_Buffalo_buffalo_buffalo_buffalo_Buffalo_buffalo)

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yongjik
To be fair, if someone actually utters that sentence, I think most English
speakers would reply with "I'm sorry?" or "Do you need help?"

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Borating
See the video TED-Ed Buffalo buffalo buffalo: One-word sentences and how they
work - Emma Bryce [1]

[1]
[https://www.youtube.com/watch?v=TWbzjGIec20](https://www.youtube.com/watch?v=TWbzjGIec20)

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mark_l_watson
Very interesting demo. I am not being critical, but it does not do anaphora
resolution (resolving pronouns to noun phrases, etc).

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meepmorp
It got "colorless green ideas sleep furiously," wrong.

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amirkdv
I don't see how it got it wrong (and it would be surprising if it did given
the sentence is not syntactically ambiguous). It does use a visual notation
which, AFAIK, differs from the standard in linguistcs [0].

[0]:
[https://en.wikipedia.org/wiki/File:Syntax_tree_for_Colorless...](https://en.wikipedia.org/wiki/File:Syntax_tree_for_Colorless_Green_Ideas_sleep_furiosly.png)

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canjobear
This site is producing dependency parses, which have become popular in the
last 10 years or so. The linked figure is a phrase structure parse, which used
to be the thing everyone worked on but have since turned out to be both harder
to get and less useful than dependency parses.

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mempko
"The cat ate the farty bird"

Sorry machine, "farty" is an adjective, not a noun.

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Chris2048
Not really..

[http://dictionary.cambridge.org/us/dictionary/english/arty-f...](http://dictionary.cambridge.org/us/dictionary/english/arty-
farty?q=%22farty%22)

