@inproceedings{CharbonnierWartena2019, author = {Jean Charbonnier and Christian Wartena}, title = {Predicting Word Concreteness and Imagery}, series = {Proceedings of the 13th International Conference on Computational Semantics - Long Papers}, editor = {Simon Dobnik and Stergios Chatzikyriakidis and Vera Demberg}, publisher = {Association for Computational Linguistics}, address = {Stroudsburg, Pennsylvania}, doi = {10.25968/opus-1359}, url = {http://nbn-resolving.de/urn:nbn:de:bsz:960-opus4-13591}, pages = {176 -- 187}, year = {2019}, 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.}, language = {en} }