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 |
|---|---|
| URN: | urn:nbn:de:bsz:960-opus4-13591 |
| URL: | https://www.aclweb.org/anthology/W19-0415 |
| DOI: | https://doi.org/10.25968/opus-1359 |
| 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 |
| Language: | English |
| 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): |  Creative Commons - CC BY - Namensnennung 4.0 International |
