TY - GEN
T1 - Fully unsupervised core-adjunct argument classification
AU - Abend, Omri
AU - Rappoport, Ari
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=80053220989&partnerID=8YFLogxK
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AN - SCOPUS:80053220989
SN - 9781617388088
T3 - ACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
SP - 226
EP - 236
BT - ACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
T2 - 48th Annual Meeting of the Association for Computational Linguistics, ACL 2010
Y2 - 11 July 2010 through 16 July 2010
ER -