This is exactly the type of problem that a good parser should be able to solve, and training a parser on lots of data and throwing neural nets may indeed be a viable solution. Why wouldn&#x27;t it be? The article describes how their architecture can help make sense of ambiguity.<p>In terms of a basic probabilistic model, P(meow | rug) would be far lower than P(meow | cat), and that alone would be enough to influence the parser to make the correct decision. Now, if the sentence were &quot;The cat sat on the rug. It was furry&quot;, that would be more ambiguous, just like it is for an actual human to decode. But models trained on real-world data <i>do</i> learn about the world.