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    Semantic classification of noun phrases using web counts and learning algorithms

    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.

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    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: School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Sarah Hall
    Date Deposited: 13 Jul 2021 14:51
    Last Modified: 13 Jul 2021 14:51
    URI: https://eprints.bbk.ac.uk/id/eprint/45092

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