Paper: Fully Unsupervised Core-Adjunct Argument Classification

ACL ID P10-1024
Title Fully Unsupervised Core-Adjunct Argument Classification
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2010
Authors

The core-adjunct argument distinction is a basic one in the theory of argument struc- ture. The task of distinguishing between the two has strong relations to various ba- sic NLP tasks such as syntactic parsing, semantic role labeling and subcategoriza- tion 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.