Pubblicazione scientifica

Visually Grounded Meaning Representations

FILONE TECNOLOGICO	TEMA
produzione e cattura

RIASSUNTO

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 700 K 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.

DATA

Data di pubblicazione: 02/12/2016

AUTORI

CARINA SILBERER
VITTORIO FERRARI
MIRELLA LAPATA

ENTE DI AFFERENZA

SCH OF INF UNIV EDINBURGH EDINBURGH UK

RIVISTA

IEEE Transactions on Pattern Analysis and Machine Intelligence (Volume: 39, Issue: 11, Page(s): 2284-2297)

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