@inproceedings{charbonnier-wartena-2019-predicting,
title = "Predicting Word Concreteness and Imagery",
author = "Charbonnier, Jean and
Wartena, Christian",
editor = "Dobnik, Simon and
Chatzikyriakidis, Stergios and
Demberg, Vera",
booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Long Papers",
month = may,
year = "2019",
address = "Gothenburg, Sweden",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-0415",
doi = "10.18653/v1/W19-0415",
pages = "176--187",
abstract = "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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%0 Conference Proceedings
%T Predicting Word Concreteness and Imagery
%A Charbonnier, Jean
%A Wartena, Christian
%Y Dobnik, Simon
%Y Chatzikyriakidis, Stergios
%Y Demberg, Vera
%S Proceedings of the 13th International Conference on Computational Semantics - Long Papers
%D 2019
%8 May
%I Association for Computational Linguistics
%C Gothenburg, Sweden
%F charbonnier-wartena-2019-predicting
%X 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.
%R 10.18653/v1/W19-0415
%U https://aclanthology.org/W19-0415
%U https://doi.org/10.18653/v1/W19-0415
%P 176-187
Markdown (Informal)
[Predicting Word Concreteness and Imagery](https://aclanthology.org/W19-0415) (Charbonnier & Wartena, IWCS 2019)
ACL
- Jean Charbonnier and Christian Wartena. 2019. Predicting Word Concreteness and Imagery. In Proceedings of the 13th International Conference on Computational Semantics - Long Papers, pages 176–187, Gothenburg, Sweden. Association for Computational Linguistics.