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&#x27;s a paper on the Stanford CoreNLP parser, which you can compare with Google&#x27;s paper: <a href="http:&#x2F;&#x2F;cs.stanford.edu&#x2F;people&#x2F;danqi&#x2F;papers&#x2F;emnlp2014.pdf" rel="nofollow">http:&#x2F;&#x2F;cs.stanford.edu&#x2F;people&#x2F;danqi&#x2F;papers&#x2F;emnlp2014.pdf</a><p>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&#x27;ve provided. I don&#x27;t think Google&#x27;s model has a better sense of humor than the others, no. I anticipate that they all have used relatively similar training data.<p>However, there is probably a trivial way to get the second sentence to parse as<p><pre><code>      Subject --- Verb --- Object
     Noun       Verb   Article  Noun
      |   \       |     |        |
    Fruit flies  like   a      banana .
</code></pre>
and that is to provide training data with more occurrences of ...<p><pre><code>  &gt; N{Fruit flies} V{like} honey. 
  &gt; N{Fruit flies} V{like} sugar water.
</code></pre>
than occurrences of<p><pre><code>  &gt; A plane V{flies} PREP{like} a bird.
</code></pre>
The more sentences using simile that the parser finds, the less likely the neural net is to consider &#x27;like&#x27; as a verb. It&#x27;s also impacted by all of the uses of [flies like].<p>That&#x27;s the nature of statistical language tools.<p>The stock parser debuted here gives the same answer as CoreNLP, by the way.<p><pre><code>    flies VBZ ROOT
     +-- Fruit NNP nsubj
     +-- like IN prep
     |   +-- banana NN pobj
     |       +-- a DT det
     +-- . . punct
</code></pre>
So much for Parsey McParseface&#x27;s sense of humor.