Nulty, Paul (2007) Semantic classification of noun phrases using web counts and learning algorithms. In: Carroll, J.A. and van den Bosch, A. and Zaenen, A. (eds.) Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics: ACL 2007. The Association for Computational Linguistics, pp. 79-84.
Abstract
This paper investigates the use of machine learning algorithms to label modifier-noun compounds with a semantic relation. The attributes used as input to the learning algorithms are the web frequencies for phrases containing the modifier, noun, and a prepositional joining term. We compare and evaluate different algorithms and different joining phrases on Nastase and Szpakowicz’s (2003) dataset of 600 modifier-noun compounds. We find that by using a Support Vector Machine classifier we can obtain better performance on this dataset than a current state-of-the-art system; even with a relatively small set of prepositional joining terms.
Metadata
| Item Type: | Book Section |
|---|---|
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Depositing User: | Sarah Hall |
| Date Deposited: | 13 Jul 2021 14:51 |
| Last Modified: | 09 Aug 2023 12:51 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/45092 |
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