3 ms·

I mean fully online, where I don't have to download and setup tensorflow.

xigency 10y ago
Yes, probably a few days, unless you go through the effort to setup a web server. For other non-Parsey McParseface dependency parsers and POS taggers that are web accessible, see http://corenlp.run/ http://corenlp.run/ and http://nlp.stanford.edu:8080/parser/ http://nlp.stanford.edu:8080/parser/.
- astrange 10y ago
  Does Google's have a better sense of humor than the 3 in this thread? They all fail on: Time flies like an arrow. Fruit flies like a banana.
  - xigency 10y ago
    Really, the mechanism of all these parsers, including SyntaxNet, is the same in that they use statistical training data to set up a neural network. Here's a paper on the Stanford CoreNLP parser, which you can compare with Google's paper: http://cs.stanford.edu/people/danqi/papers/emnlp2014.pdf http://cs.stanford.edu/people/danqi/papers/emnlp2014.pdf So, really all of the above parsers are weak in that they only output a single best parsing, when in reality sentences can have more than one valid structure, the principal example being the second sentence you've provided. I don't think Google's model has a better sense of humor than the others, no. I anticipate that they all have used relatively similar training data. However, there is probably a trivial way to get the second sentence to parse as Subject --- Verb --- Object Noun Verb Article Noun | \ | | | Fruit flies like a banana . and that is to provide training data with more occurrences of ... > N{Fruit flies} V{like} honey. > N{Fruit flies} V{like} sugar water. than occurrences of > A plane V{flies} PREP{like} a bird. The more sentences using simile that the parser finds, the less likely the neural net is to consider 'like' as a verb. It's also impacted by all of the uses of [flies like]. That's the nature of statistical language tools. The stock parser debuted here gives the same answer as CoreNLP, by the way. flies VBZ ROOT +-- Fruit NNP nsubj +-- like IN prep | +-- banana NN pobj | +-- a DT det +-- . . punct So much for Parsey McParseface's sense of humor.

Yes, probably a few days, unless you go through the effort to setup a web server. For other non-Parsey McParseface dependency parsers and POS taggers that are web accessible, see http://corenlp.run/ http://corenlp.run/ and http://nlp.stanford.edu:8080/parser/ http://nlp.stanford.edu:8080/parser/.

astrange 10y ago
Does Google's have a better sense of humor than the 3 in this thread? They all fail on: Time flies like an arrow. Fruit flies like a banana.
- xigency 10y ago
  Really, the mechanism of all these parsers, including SyntaxNet, is the same in that they use statistical training data to set up a neural network. Here's a paper on the Stanford CoreNLP parser, which you can compare with Google's paper: http://cs.stanford.edu/people/danqi/papers/emnlp2014.pdf http://cs.stanford.edu/people/danqi/papers/emnlp2014.pdf So, really all of the above parsers are weak in that they only output a single best parsing, when in reality sentences can have more than one valid structure, the principal example being the second sentence you've provided. I don't think Google's model has a better sense of humor than the others, no. I anticipate that they all have used relatively similar training data. However, there is probably a trivial way to get the second sentence to parse as Subject --- Verb --- Object Noun Verb Article Noun | \ | | | Fruit flies like a banana . and that is to provide training data with more occurrences of ... > N{Fruit flies} V{like} honey. > N{Fruit flies} V{like} sugar water. than occurrences of > A plane V{flies} PREP{like} a bird. The more sentences using simile that the parser finds, the less likely the neural net is to consider 'like' as a verb. It's also impacted by all of the uses of [flies like]. That's the nature of statistical language tools. The stock parser debuted here gives the same answer as CoreNLP, by the way. flies VBZ ROOT +-- Fruit NNP nsubj +-- like IN prep | +-- banana NN pobj | +-- a DT det +-- . . punct So much for Parsey McParseface's sense of humor.