Changing morphological structures: The effect of sentence context on the interpretation of structurally ambiguous English trimorphemic words

Abstract

Morphological parsing has often been studied with words in isolation. In this study we used sentence context to investigate how structural analyses of morphologically complex words are affected by the semantic content of their carrier sentences. Our main stimuli were trimorphemic ambiguous words such as unlockable (meaning either “not able to be locked” or “able to be unlocked”). We treat these words as structurally ambiguous such that the meaning of the words is determined by the perceived organisation of their constituent morphemes. The effect and malleability of this structural organisation were examined in one offline rating experiment and one cross-modal priming experiment with ambiguous words embedded in sentence context. The results of the study suggest that morphologically ambiguous words do show two interpretations and that the balance of these interpretations can be affected by the semantics of the sentence in which they are embedded. We interpret the pattern of data to suggest that when structurally ambiguous words are presented in isolation, word-internal factors determine which interpretation is to be preferred. However, in strongly constraining sentence contexts, these preferred parses are modified online to be consistent with the semantics of the entire sentence structure.

Acknowledgments

Authors are listed in alphabetical order. This research was supported by research grants from the Social Sciences and Humanities Research Council of Canada (SSHRC) and the Fonds quebecois de la recherche sur la societe et la culture to Roberto G de Almeida and a Major Collaborative Research Initiative Grant from SSHRC to Gary Libben (Director) Gonia Jarema, Eva Kehayia, Bruce Derwing, and Lori Buchanan.

We would like to thank Julie Turbide for her help in the preparation of the materials and for recording the sentences for Experiment 2. We also thank our research assistants for helping with data coding and running the experiments: Julie Turbide, Heather Wilcox, and Sally Cooper. Finally, we would like to thank Harald Baayen, Jonathan Grainger, and an anonymous reviewer for many helpful comments and suggestions.