Towards an exemplar-based model of stress in English noun–noun compounds
Bibliographic Data
| ID | 10141376 |
|---|---|
| Authors | Sabine Arndt-Lappe (0000-0001-7723-7151, Folkwang University of the Arts, corresponding author) |
| Year | 2011 |
| Volume | 47 |
| Issue | 3 |
| Pages | 549-585 |
| Publication date | 2011-05-12 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Linguistics (JOURNAL) |
| Journal identifiers | ISSN: 0022-2267 • E-ISSN: 1469-7742 |
| Publisher | Cambridge University Press (CUP) (PUBLISHER) |
| DOI | 10.1017/s0022226711000028 |
| OpenAlex | W2137678802 |
| Language | EN |
| Citations received | 12 |
| References cited | 21 |
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)
Argument (complex analysis · Categorical variable · Grammatical Gender · Linguistics · Machine learning · Natural language processing · Noun · Semantics (computer science · Stress (linguistics · Artificial Intelligence · Computer Science · Mathematics · Natural Language Processing Techniques · Philosophy · Phonetics and Phonology Research · Syntax, Semantics, Linguistic Variation
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| Unique citing works | 12 |
|---|---|
| Citations per year | 0,8 |
| Citation span | 2011 - 2023 (13) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 12 |