Foundations of statistical natural language processing
Foundations of statistical natural language processing
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
The Journal of Machine Learning Research
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Word sense disambiguation using Conceptual Density
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
HLT '91 Proceedings of the workshop on Speech and Natural Language
Structured multimedia document classification
Proceedings of the 2003 ACM symposium on Document engineering
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Word sense disambiguation with pictures
Artificial Intelligence - Special volume on connecting language to the world
Fast unsupervised alignment of video and text for indexing/names and faces
Workshop on multimedia information retrieval on The many faces of multimedia semantics
Toward communicating simple sentences using pictorial representations
Machine Translation
Word sense disambiguation with pictures
Artificial Intelligence - Special volume on connecting language to the world
Unsupervised disambiguation of image captions
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
picoTrans: An intelligent icon-driven interface for cross-lingual communication
ACM Transactions on Interactive Intelligent Systems (TiiS) - Special section on internet-scale human problem solving and regular papers
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We introduce a method for using images for word sense disambiguation, either alone, or in conjunction with traditional text based methods. The approach is based in recent work on a method for predicting words for images which can be learned from image datasets with associated text. When word prediction is constrained to a narrow set of choices such as possible senses, it can be quite reliable, and we use these predictions either by themselves or to reinforce standard methods. We provide preliminary results on a subset of the Corel image database which has three to five keywords per image. The subset was automatically selected to have a greater portion of keywords with sense ambiguity and the word senses were hand labeled to provide ground truth for testing. Results on this data strongly suggest that images can help with word sense disambiguation.