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Visually grounded meaning representations
Silberer, Carina
;
Ferrari, Vittorio
;
Lapata, Mirella
. - : Institute of Electrical and Electronics Engineers (IEEE)
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 representations from textual and visual input. The visual modality is encoded via vectors of attributes obtained automatically from images. We create a new large-scale taxonomy of 600 visual attributes representing more than 500 concepts and 700K images. We use this dataset to train attribute classifiers and integrate their predictions with text-based distributional models of word meaning. We evaluate our model on its ability to simulate word similarity judgments and concept categorization. On both tasks, our model yields a better fit to behavioral data compared to baselines and related models which either rely on a single modality or do not make use of attribute-based input.
Keyword:
Cognitive simulation
;
Computer vision
;
Concept learning
;
Connectionism and neural nets
;
Distributed representations
;
Naturallanguage processing
URL:
http://hdl.handle.net/10230/41865
https://doi.org/10.1109/TPAMI.2016.2635138
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