Revisiting the Prepositional-Phrase Attachment Problem Using Explicit Commonsense Knowledge

TL;DR

This paper explores using explicit commonsense knowledge bases to improve prepositional-phrase attachment resolution, combining rule-based and statistical methods for better accuracy and explainability.

Contribution

It introduces Patch-Comm, a module leveraging commonsense KBs for attachment decisions, enhancing parser performance and interpretability.

Findings

Commonsense KBs improve attachment accuracy.

The approach integrates rule-based and statistical methods.

Enhanced explainability of NLP systems.

Abstract

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.