  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.
