Abstract
This study argues that a productive but not fully regular morphological
phenomenon, the choice of linking morphemes in Dutch nominal compounds,
is based on analogy. In Dutch, a linking -s- or -en- can appear between the
constituents of a nominal compound. We present production experiments
that reveal strong evidence that the choice of linking morphemes in novel
compounds is analogically determined by the distribution of linking morphemes
in what we call the ‘‘constituent families.’’ A ‘‘constituent family’’
is the set of existing compounds that share the first (or second) constituent
with the novel compound. A further experiment shows that in the case of
derived pseudo-words as first constituents, it is the family of the suffix that
influences the choice of the following linking morpheme. In addition to these
experiments, we present computational simulation studies in which the
choices made by participants in our experiments are predicted with a high
degree of accuracy using a machine-learning algorithm for analogy. These
studies support the status of the constituent family as the primary basis for
analogical prediction. Finally, we outline a psycholinguistic model for analogy
in the mental lexicon that does not give up symbolic representations
and, at the same time, captures nondeterministic variation.
phenomenon, the choice of linking morphemes in Dutch nominal compounds,
is based on analogy. In Dutch, a linking -s- or -en- can appear between the
constituents of a nominal compound. We present production experiments
that reveal strong evidence that the choice of linking morphemes in novel
compounds is analogically determined by the distribution of linking morphemes
in what we call the ‘‘constituent families.’’ A ‘‘constituent family’’
is the set of existing compounds that share the first (or second) constituent
with the novel compound. A further experiment shows that in the case of
derived pseudo-words as first constituents, it is the family of the suffix that
influences the choice of the following linking morpheme. In addition to these
experiments, we present computational simulation studies in which the
choices made by participants in our experiments are predicted with a high
degree of accuracy using a machine-learning algorithm for analogy. These
studies support the status of the constituent family as the primary basis for
analogical prediction. Finally, we outline a psycholinguistic model for analogy
in the mental lexicon that does not give up symbolic representations
and, at the same time, captures nondeterministic variation.
Original language | English |
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Pages (from-to) | 51-93 |
Number of pages | 42 |
Journal | Linguistics |
Volume | 39 |
Issue number | 1 |
DOIs | |
Publication status | Published - 2001 |