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Predicting Word Concreteness and Imagery

  • Concreteness of words has been studied extensively in psycholinguistic literature. A number of datasets have been created with average values for perceived concreteness of words. We show that we can train a regression model on these data, using word embeddings and morphological features, that can predict these concreteness values with high accuracy. We evaluate the model on 7 publicly available datasets. Only for a few small subsets of these datasets prediction of concreteness values are found in the literature. Our results clearly outperform the reported results for these datasets.

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Author:Jean CharbonnierORCiD, Christian WartenaORCiDGND
Parent Title (English):Proceedings of the 13th International Conference on Computational Semantics - Long Papers
Publisher:Association for Computational Linguistics
Place of publication:Stroudsburg, Pennsylvania
Editor:Simon Dobnik, Stergios Chatzikyriakidis, Vera Demberg
Document Type:Conference Proceeding
Year of Completion:2019
Publishing Institution:Hochschule Hannover
Release Date:2019/07/23
Tag:Concreteness; Distributional Semantics; Imagery; Lexical Semantics
GND Keyword:Konkretum <Linguistik>
First Page:176
Last Page:187
Link to catalogue:1689961767
Institutes:Fakultät III - Medien, Information und Design
DDC classes:020 Bibliotheks- und Informationswissenschaft
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International