echo 'Bob brought the pizza to Alice.' | syntaxnet/demo.sh
Input: Bob brought the pizza to Alice .
Parse:
brought VBD ROOT
+-- Bob NNP nsubj
+-- pizza NN dobj
| +-- the DT det
+-- to IN prep
| +-- Alice NNP pobj
+-- . . punct
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
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 ...
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