@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://preview.aclanthology.org/iwcs-25-ingestion/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."
}