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Article published In: Explorations of morphological structure in distributional space
Edited by Melanie J. Bell, Juhani Järvikivi and Vito Pirrelli
[The Mental Lexicon 17:3] 2022
► pp. 422–457

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An inquiry into the semantic transparency and productivity of German particle verbs and derivational affixation

 | University of Tübingen
ORCiD logo with link | University of Tübingen
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Available under the Creative Commons Attribution (CC BY) 4.0 license.

For any use beyond this license, please contact the publisher at rights@benjamins.nl.

Published online: 23 March 2023
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https://doi.org/10.1075/ml.22012.stu
Abstract
This study addresses the relation between morphological productivity and semantic transparency. Using distributional semantics, we compare German word formation using particles with derivational word formation. We observed that derivational suffixes, but not particles, tend to make strong independent semantic contributions to their carrier words. In two-dimensional t-SNE maps, complex words show clustering by affix, but not by particle. Furthermore, the semantic vectors of suffixed words are predictable from their base words with higher accuracy than is possible for particle verbs. For particle verbs, but not affixed verbs, semantic similarity within the set of complex words correlated negatively with the number of types. Furthermore, only for particle verbs, a greater number of observed types predicted a reduced probability of observing unseen types. We propose that particle verbs primarily serve the onomasiological function of labeling, resulting in relatively idiosyncratic semantic vectors. By contrast, words sharing derivational affixes form distinct clusters in semantic space while maintaining strong and consistent semantic relations with their base words. This enables these words to serve not only as labels, but also allows them to be used with an anaphoric function in discourse.
Article outline
  • 1.Introduction
  • 2.Productivity
  • 3.Word embeddings, particle verbs and affixed words
    • 3.1Semantic transparency assessed with constituent vectors
    • 3.2Assessing transparency with averaged within-category correlations
  • 4.Geometry of semantic transparency in semantic space
    • 4.1t-SNE analysis of semantic space
    • 4.2t-SNE analysis of shift vectors
  • 5.Assessing transparency with functions for conceptualization
  • 6.Discussion
  • Acknowledgements
  • Notes
  • References
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