  We revisit the challenging problem of resolving prepositional-phrase (PP)
attachment ambiguity. To date, proposed solutions are either rule-based, where
explicit grammar rules direct how to resolve ambiguities; or statistical, where
the decision is learned from a corpus of labeled examples. We argue that
explicit commonsense knowledge bases can provide an essential ingredient for
making good attachment decisions. We implemented a module, named Patch-Comm,
that can be used by a variety of conventional parsers, to make attachment
decisions. Where the commonsense KB does not provide direct answers, we fall
back on a more general system that infers "out-of-knowledge-base" assertions in
a manner similar to the way some NLP systems handle out-of-vocabulary words.
Our results suggest that the commonsense knowledge-based approach can provide
the best of both worlds, integrating rule-based and statistical techniques. As
the field is increasingly coming to recognize the importance of explainability
in AI, a commonsense approach can enable NLP developers to better understand
the behavior of systems, and facilitate natural dialogues with end users.
