PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Fully Unsupervised Core-Adjunct Argument Classification.
Omri Abend and Ari Rappoport
ACL 2010 2010.

Abstract

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.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:7067
Deposited By:Ari Rappoport
Deposited On:27 February 2011