Fully Unsupervised Core-Adjunct Argument Classification.
Omri Abend and Ari Rappoport
The core-adjunct argument distinction is a
basic one in the theory of argument structure.
The task of distinguishing between
the two has strong relations to various basic
NLP tasks such as syntactic parsing,
semantic role labeling and subcategorization
acquisition. This paper presents a
novel unsupervised algorithm for the task
that uses no supervised models, utilizing
instead state-of-the-art syntactic induction
algorithms. This is the first work to tackle
this task in a fully unsupervised scenario.