That is not necessarily true.  The problem of ambiguity is fundamental to natural language processing, and a lot of research goes in to addressing it.  If we also see a sentence where unambiguously the word &quot;cat&quot; is the subject of the verb &quot;meow&quot;, then this could give our parser clues about ambiguous attachment or, in this case, anaphora resolution (to what does &quot;it&quot; refer).  In any intro NLP class, you will learn about lexicalized parsing, which takes the head word of the phrase into account when making parsing decisions.  I haven&#x27;t read the paper on this parser yet, but I don&#x27;t think it is hard to see that your sentence could be accurately parsed given enough data.  Look up &quot;word embeddings&quot; for instance, which are fundamental to deep learning for NLP and could probably be trained to assist in disambiguating anaphora or attachment.