Towards an exemplar-based model of stress in English noun-noun compounds
Identifieur interne : 000C15 ( Main/Merge ); précédent : 000C14; suivant : 000C16Towards an exemplar-based model of stress in English noun-noun compounds
Auteurs : Sabine Arndt-Lappe [Allemagne]Source :
- Journal of linguistics [ 0022-2267 ] ; 2011.
Descripteurs français
- Pascal (Inist)
English descriptors
- KwdEn :
Abstract
It is well known that stress assignment in English noun-noun compounds is non-uniform (compare e.g. left-prominent ópera glasses and right-prominent steel bridge), and recent corpus-based studies (e.g. Plag et al. 2007, 2008) have shown that categorical, rule-based approaches that make use of argument structure (e.g. Giegerich 2004) or semantics (e.g. Fudge 1984) are not able to account satisfactorily for the existing variability. Using data from the corpus studies by Plag and collegues, I argue in this paper that an exemplar-based approach is better-suited to accounting for stress assignment in English noun-noun compounds than a traditional, rule-based paradigm. Specifically, it is shown that two current implementations of exemplar-based algorithms, TiMBL (Daelemans et al. 2007) and AM::Parallel (Skousen & Stanford 2007), clearly outperform comparable rule models in terms of how well they predict stress assignment in the corpora. Furthermore, systematic testing reveals that the reasons for the differences between exemplar and rule models mainly lie in their ability to incorporate detailed, non-abstract information (specifically, constituent family information). The present study therefore adds to the growing evidence in favour of the importance of constituent family information in compounding (e.g. Gagne 200I, Krott, Schreuder & Baayen 2002).
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Francis:12-0245472Le document en format XML
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<front><div type="abstract" xml:lang="en">It is well known that stress assignment in English noun-noun compounds is non-uniform (compare e.g. left-prominent ópera glasses and right-prominent steel bridge), and recent corpus-based studies (e.g. Plag et al. <sub>2007</sub>
, <sub>200</sub>
8) have shown that categorical, rule-based approaches that make use of argument structure (e.g. Giegerich <sub>2004</sub>
) or semantics (e.g. Fudge <sub>19</sub>
8<sub>4</sub>
) are not able to account satisfactorily for the existing variability. Using data from the corpus studies by Plag and collegues, I argue in this paper that an exemplar-based approach is better-suited to accounting for stress assignment in English noun-noun compounds than a traditional, rule-based paradigm. Specifically, it is shown that two current implementations of exemplar-based algorithms, TiMBL (Daelemans et al. <sub>2007</sub>
) and AM::Parallel (Skousen & Stanford 2007), clearly outperform comparable rule models in terms of how well they predict stress assignment in the corpora. Furthermore, systematic testing reveals that the reasons for the differences between exemplar and rule models mainly lie in their ability to incorporate detailed, non-abstract information (specifically, constituent family information). The present study therefore adds to the growing evidence in favour of the importance of constituent family information in compounding (e.g. Gagne 200I, Krott, Schreuder & Baayen 2002).</div>
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