A statistical approach to machine translation
Computational Linguistics
Using multiple knowledge sources for word sense discrimination
Computational Linguistics
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
An Iterative Approach to Word Sense Disambiguation
Proceedings of the Thirteenth International Florida Artificial Intelligence Research Society Conference
Automatic image annotation and retrieval using cross-media relevance models
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Statistical Models for Co-occurrence Data
Statistical Models for Co-occurrence Data
The Journal of Machine Learning Research
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
Word-sense disambiguation using statistical methods
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Integrating multiple knowledge sources to disambiguate word sense: an exemplar-based approach
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
Word-sense disambiguation using statistical models of Roget's categories trained on large corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
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
HLT '93 Proceedings of the workshop on Human Language Technology
Word sense disambiguation with pictures
HLT-NAACL-LWM '04 Proceedings of the HLT-NAACL 2003 workshop on Learning word meaning from non-linguistic data - Volume 6
Finding predominant word senses in untagged text
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Hierarchical semantic classification: word sense disambiguation with world knowledge
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A WordNet-based algorithm for word sense disambiguation
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - 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
Discriminating image senses by clustering with multimodal features
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Improving Web search using image snippets
ACM Transactions on Internet Technology (TOIT)
Improve web search using image snippets
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Learning to connect language and perception
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Utilizing Images for Assisting Cross-Language Information Retrieval on the Web
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Challenges for annotating images for sense disambiguation
LAC '06 Proceedings of the Workshop on Frontiers in Linguistically Annotated Corpora 2006
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We introduce using images for word sense disambiguation, either alone, or in conjunction with traditional text based methods. The approach is based on a recently developed method for automatically annotating images by using a statistical model for the joint probability for image regions and words. The model itself is learned from a data base of images with associated text. To use the model for word sense disambiguation, we constrain the predicted words to be possible senses for the word under consideration. When word prediction is constrained to a narrow set of choices (such as possible senses), it can be quite reliable. We report on experiments using the resulting sense probabilities as is, as well as augmenting a state of the art text based word sense disambiguation algorithm. In order to evaluate our approach, we developed a new corpus, ImCor, which consists of a substantive portion of the Corel image data set associated with disambiguated text drawn from the SemCor corpus. Our experiments using this corpus suggest that visual information can be very useful in disambiguating word senses. It also illustrates that associated non-textual information such as image data can help ground language meaning.