Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Placing search in context: the concept revisited
ACM Transactions on Information Systems (TOIS)
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Modeling Semantic Aspects for Cross-Media Image Indexing
IEEE Transactions on Pattern Analysis and Machine Intelligence
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Multiple Bernoulli relevance models for image and video annotation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Estimating dyslexia in the web
Proceedings of the International Cross-Disciplinary Conference on Web Accessibility
Measuring the semantic relatedness between words and images
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
Distributional semantics from text and images
GEMS '11 Proceedings of the GEMS 2011 Workshop on GEometrical Models of Natural Language Semantics
Distributional semantics in technicolor
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Grounded models of semantic representation
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Proceedings of the 20th ACM international conference on Multimedia
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The question of how meaning might be acquired by young children and represented by adult speakers of a language is one of the most debated topics in cognitive science. Existing semantic representation models are primarily amodal based on information provided by the linguistic input despite ample evidence indicating that the cognitive system is also sensitive to perceptual information. In this work we exploit the vast resource of images and associated documents available on the web and develop a model of multimodal meaning representation which is based on the linguistic and visual context. Experimental results show that a closer correspondence to human data can be obtained by taking the visual modality into account.