a1 Universität Siegen
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 brídge), 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. Gagné 2001, Krott, Schreuder & Baayen 2002).
(Received October 10 2009)
(Revised December 13 2010)
(Online publication May 12 2011)
 This paper has benefitted greatly from comments and discussions from audiences at the Workshop on Exemplar-Based Models in Language Acquisition and Use, Dublin, ESSLLI 2007 (organised by Rens Bod and David Cochran), the International Morphology Meeting, Vienna 2008, and the ISLE conference, Freiburg 2008, where earlier versions were presented, as well as from helpful comments from two anonymous JL referees. Special thanks are due to Ingo Plag and Gero Kunter for comments, discussion and critical input, and to Kristina Kösling for helping me with the coding. Needless to say, all remaining errors are my own. Finally, I thank the Deutsche Forschungsgemeinschaft (Grant PL-151/5–3) for supporting this research.