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Unsupervised Learning of Visual Sense Models for Polysemous Words
Kate Saenko · Trevor Darrell

Mon Dec 08 08:45 PM -- 12:00 AM (PST) @ None #None

Polysemy is a problem for methods that exploit image search engines to build object category models. Existing unsupervised approaches do not take word sense into consideration. We propose a new method that uses a dictionary to learn models of visual word sense from a large collection of unlabeled web data. The use of LDA to discover a latent sense space makes the model robust despite the very limited nature of dictionary definitions. The definitions are used to learn a distribution in the latent space that best represents a sense. The algorithm then uses the text surrounding image links to retrieve images with high probability of a particular dictionary sense. An object classifier is trained on the resulting sense-specific images. We evaluate our method on a dataset obtained by searching the web for polysemous words. Category classification experiments show that our dictionary-based approach outperforms baseline methods.

Author Information

Kate Saenko (UMass Lowell)
Trevor Darrell (UC Berkeley)

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