Machine Learning
Dictionary Methods for Cross-Lingual Information Retrieval
DEXA '96 Proceedings of the 7th International Conference on Database and Expert Systems Applications
An empirical comparison of supervised learning algorithms
ICML '06 Proceedings of the 23rd international conference on Machine learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Compiling a massive, multilingual dictionary via probabilistic inference
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Panlingual lexical translation via probabilistic inference
Artificial Intelligence
PanLex and LEXTRACT: translating all words of all languages of the world
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Demonstrations
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We query Web Image search engines with words (e.g., spring) but need images that correspond to particular senses of the word (e.g., flexible coil). Querying with polysemous words often yields unsatisfactory results from engines such as Google Images. We build an image search engine, Idiom, which improves the quality of returned images by focusing search on the desired sense. Our algorithm, instead of searching for the original query, searches for multiple, automatically chosen translations of the sense in several languages. Experimental results show that Idiom outperforms Google Images and other competing algorithms returning 22% more relevant images.