Year: 2014
Type: conference-paper
Abstract: In this paper we address the problem of grounding distributional representations of lexical meaning. We introduce a new model which uses stacked autoencoders to learn higher-level embeddings from tex-tual and visual input. The two modali-ties are encoded as vectors of attributes and are obtained automatically from text and images, respectively. We evaluate our model on its ability to simulate similar-ity judgments and concept categorization. On both tasks, our approach outperforms baselines and related models. 1
DOI: 10.3115/v1/p14-1068
Authors: Carina Silberer, Mirella Lapata
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2250742840