WordNet: a lexical database for English
Communications of the ACM
Using a semantic concordance for sense identification
HLT '94 Proceedings of the workshop on Human Language Technology
An empirical evaluation of knowledge sources and learning algorithms for word sense disambiguation
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Learning to judge image search results
MM '11 Proceedings of the 19th ACM international conference on Multimedia
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Literal and metaphorical sense identification through concrete and abstract context
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Oracle in Image Search: A Content-Based Approach to Performance Prediction
ACM Transactions on Information Systems (TOIS)
Language Resources and Evaluation
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This paper explores how to predict query difficulty for contextual image retrieval. We reformulate the problem as the task of predicting how difficult to represent a query as images. We propose to use machine learning algorithms to learn the query difficulty prediction models based on the characteristics of the query words as well as the query context. More specifically, we focus on noun word/phrase queries and propose four features based on several assumptions. We created an evaluation data set by hand and compare several machine learning algorithms on the prediction task. Our preliminary experimental results show the effectiveness of our proposed features and the stable performance using different classification models.