Predicting the Argumenthood of English Prepositional Phrases

TL;DR

This paper develops models to predict argumenthood of English prepositional phrases, aiding NLP tasks like semantic role labeling and PP attachment, and demonstrates how argumenthood prediction improves sentence representations.

Contribution

It introduces two novel PP argumenthood prediction tasks using pretrained embeddings and features, with high accuracy and correlation, and shows their utility in enhancing sentence encoding for SRL.

Findings

High accuracy in binary argument-adjunct classification (95.5%)

Significant correlation in gradient argumenthood prediction (r=0.624)

Improved SRL performance with argumenthood-aware sentence encoders

Abstract

Distinguishing between arguments and adjuncts of a verb is a longstanding, nontrivial problem. In natural language processing, argumenthood information is important in tasks such as semantic role labeling (SRL) and prepositional phrase (PP) attachment disambiguation. In theoretical linguistics, many diagnostic tests for argumenthood exist but they often yield conflicting and potentially gradient results. This is especially the case for syntactically oblique items such as PPs. We propose two PP argumenthood prediction tasks branching from these two motivations: (1) binary argument-adjunct classification of PPs in VerbNet, and (2) gradient argumenthood prediction using human judgments as gold standard, and report results from prediction models that use pretrained word embeddings and other linguistically informed features. Our best results on each task are (1) $acc.=0.955$, $F_1=0.954$ (ELMo+BiLSTM) and (2) Pearson's $r=0.624$ (word2vec+MLP). Furthermore, we demonstrate the utility of argumenthood prediction in improving sentence representations via performance gains on SRL when a sentence encoder is pretrained with our tasks.